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    "import sys\n",
    "import os\n",
    "if not any(path.endswith('textbook') for path in sys.path):\n",
    "    sys.path.append(os.path.abspath('../../..'))\n",
    "from textbook_utils import *"
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    "(sec:scope_bigdata)=\n",
    "\n",
    "# Big Data and New Opportunities\n",
    "\n",
    "The tremendous increase in openly available data has created new roles and opportunities in data science. For example, data journalists look for interesting stories in data much like how traditional beat reporters hunt for news stories. The data lifecycle for the data journalist begins with the search for existing data that might have an interesting story, rather than beginning with a research question and looking for how to collect new or use existing data to address the question.\n"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "Citizen science projects are another example. They engage many people (and instruments) in data collection. Collectively, these data are made available to researchers who organize the project, and often they are made available in repositories for the general public to further investigate.\n"
   ]
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   "cell_type": "markdown",
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    "The availability of administrative and organizational data creates other opportunities. Researchers can link data collected from scientific studies with, say, medical data that have been collected for health-care purposes; these administrative data have been collected for reasons that don't directly stem from the question of interest, but they can be useful in other settings. Such linkages can help data scientists expand the possibilities of their analyses and cross-check the quality of their data. In addition, found data can include digital traces, such as your web-browsing activity, your posts on social media, and your online network of friends and acquaintances, and they can be quite complex.\n"
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    "When we have large amounts of administrative data or expansive digital traces, it can be tempting to treat them as more definitive than data collected from traditional, smaller research studies. We might even consider these large datasets to be a replacement for scientific studies and essentially a census. This overreach is referred to as the [\"big data hubris\"](https://doi.org/10.1126/science.1248506). Data with a large scope does not mean that we can ignore foundational issues of how representative the data are, nor can we ignore issues with measurement, dependency, and reliability. (And it can be easy to discover meaningless or nonsensical relationships just by coincidence.) One well-known example is the Google Flu Trends tracking system.\n"
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   "cell_type": "markdown",
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   "source": [
    "## Example: Google Flu Trends\n",
    "\n",
    "[Digital epidemiology](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5754279/), a new subfield of epidemiology, leverages data generated outside the public health system to study patterns of disease and health dynamics in populations.\n",
    "The Google Flu Trends (GFT) tracking system was one of the earliest examples of digital epidemiology.\n",
    "In 2007, researchers found that counting the searches people made for flu-related\n",
    "terms could accurately estimate the number of flu cases.\n",
    "This apparent success made headlines, and many researchers became excited about the possibilities of big data.\n",
    "However, GFT did not live up to expectations and was abandoned in 2015.\n",
    "\n",
    "What went wrong? After all, GFT used millions of digital traces from online queries for terms related to influenza to predict flu activity. Despite initial success, in the 2011–2012 flu season, Google's data scientists found that GFT was not a substitute for the more traditional surveillance reports of three-week-old counts collected by the US Centers for Disease Control and Prevention (CDC) from laboratories across the country. In comparison, GFT overestimated the CDC numbers for 100 out of 108 weeks. Week after week, GFT came in too high for the cases of influenza, even though it was based on big data:\n"
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aJOxSFC1aVLCLdjiICPj5+aFdu3aCTtoZoAf+9evXC/qLFCmCv/76C48ePRKOP5E9s2bNwsiRI+Hm5paiLvpuoiNXmzdvFl6+k4yaNWti4MCBgo7AwEBhh4SIkViaTMJARr97905gbEQcjhw5IrAsmiAlk9DWj6Y0Jgya4qnM28mEIfMYaoMEJgza4MXMz4EJQ+YxTEtCu4HjsG/NXNRo3QOX92+S6v7rVzz09HQB6Ahvi5PmN6QlV9Gfc2xIQLT+lvo4G3g2VXjPup+Fs41zqn0UIYd8Qg/JdMSIHtzTQxjobTzlMNBDO/196NChePLkCXR1ab2l3YgweHt7w9PTM3EMkQ4bGxu4u7sLAui4ED340wty+jsRhqdPnyYmUhPR6NevH5o0aSLYv2TJEjRv3lwYS+OIwNCzc0q6fj+S9O3bN1SqVAkjRowQ9FP+hdiaTMJAQJDRnTp1EhggbanQ1smdO3cEJnf58mVYWlqKbT4p2sOEQSPcpBAjOSgoBEaNF8KEQeNdqJAJMGFQCIypCqne0h3XD21Bh8ET4bliRrK+esam+BX1HQFvPsK2kPqeGTg2JLhGETsDipIj2WGg40j00J0ewkAP7s2aNcO5c+cQFBSU7udSeoin3AZ6wy9pTk5OwoM6HQdK2latWiW8QP+dMBBJsbCwQPfu3YUdklOnTgmEgxo9M9PuAJ3WSUlXSjkM27ZtE17SU3NwcBB2KKpWrar8/8RyapBJGGgLqFevXsL5Lfo7JaS0bt0aL1++FLZv6AwXnRnThMaEQRO8pBgbOSgoBkdNl8KEQdM9qBj7mTAoBsfUpBSv1hAvbpzG2AXrMGfUn8m6muQuICRDn7t5D3WrlFO+QTI0cGxQG/SpKm7Tpo1wemX16tVS/YhM0GmWlHIYqCOd+SeyQA/99H1fpUoV4Xd0BChpo92HbNmyJbMhpYd4OmpEz7k9evRI1l9yJCnpDkOtWrUEokLPyaR/3bp1iTc6LVu2TNhdIBKRki46vtSiRQs8ePBASPSWNPrOouNWGzduxI0bN4RjSZTjIIYmkzAQ6IUKFcKcOXMEJ9COwvjx43Hy5El4eXkJySKmpqZimEOaNjBhSBMirenAQUFrXJmpiTBhyBR8WjOYCYPyXZm7aDmEBD7Ahn3e6NUm+Tn0XEXsEPb6MTwOnkL3Fg2UbxATBrVhnBHF9EBMp1n69u0rPKibmJgIycB0u1HSW5K2b98u5CdQjN+3b5/wBn///v0oVy6BhNIuxYoVK4S8hEaNGoFqOty8eVPmLUkpPcSvWbNG2HH4559/hKtbaVeBjixRXoKEMBAJyJcvH/bu3Yv58+cLtzWVLl1aIA70TEzXw4aGhgov2V1cXDB69OgUCQPZV758eWzdulU4WvX+/XvhiBTdGEV5E3QrFCVA0xzEcouSTMJAoFDSBU2aHERMSHK37ZgxY9CnT5+MrA21jGHCoBbY1aKUCYNaYBedUiYMonOJWgxiwqB82I3MLRH9NQjX/B6jWvlSyRQWtK+O9w+vY84/OzG2byflG8SEQW0YZ1Qx3YBEZ/4pwVnS6LgRvaymWzqT1mGg3Qi6SpWeQemhXtJiYmLg4eEhJCpLbvZMrQ4DvcGnF+H0p6RFR0cLD+l0S5Kk0TElOiokIQx0BEnyLEzJ0JLLgehmo0GDBiXWkiCbSRbdmJSSLpJPuxBkLzXKf6B/0ykeapQCMHz4cOGKWLE0uQu30fYQJZTQtVYEgCY1Jgya5K3M2cqEIXP4actoJgza4snMzYMJQ+bwS2u0kNRM16v/+oUfUVEwNkx+1XqpGk3w9Ko3hs1YjqUTh6QlUmmfc2xQGrQKE0yJv3SdKe0kZOatOt1SRM+slIsgbxJ00knQWCorQDeB0o4HNQlhoKNEdIUq7QKkdFSI1hld4yrvLaK000BERdKf5k/6iZiIraV6rSpNmrZakjYCiu6spe2XzDhUlUAwYVAl2urVxUFBvfiLRTsTBrF4Qr12MGFQLv4Pn7+CfQkb6JuaIyYiobru761qs664dWwHOg2bhJ1LpyvXoFSkc2xQG/RaoTilHAatmFg6JiGTMFAxCtom+r0QhiTpmeswpANl7qoyBDgoqAxqUStiwiBq96jMOCYMyoXa8/gFdGpaF2bWpfH11aMUlbl0H4ITW1eisdtgeG9ZoVyDmDCoDV9tV0zHkCjJmoqpZdWWKmGge2c/fvwoXCVF10PRjgMThqy6VDRj3kwYNMNPyraSCYOyEdYM+UwYlOunwdOWYNXUv0DHjh5fPpaisq7Dp2LHsmlwbNoZt47uUK5BTBjUhi8r1n4EUiUMlMFNNRiokARVoaMMcvoCpvtmeYdB+xeHJs6QCYMmek3xNjNhUDymmiiRCYNyvVapUXvcPbUX3UZMxdbFKVekHTFrBZZOHIqSf7jgyZXjyjWICYPa8GXF2o9AmoSBKj7TrsKAAQPw4cMH4XqpqVOnMmHQ/rWhkTNkwqCRblO40UwYFA6pRgpkwqBct+UoUAzfPgRg17Hz6NikTorK5v67C+P+7Iz8ZZ3w/sF15RrEhEFt+LJi7UdALsJAMNA1VePGjcPx4wlvCHiHQfsXhybOkAmDJnpN8TYzYVA8ppookQmD8rz2ITgUBfLlgY6ePqJ//oC+nm6KyrYdPgO3Fg1SzXNQnpX/SebYoAqUWYc2IyCTMCxduhS2tragyneSFh8fj/Xr12P37t1CwQxNuV6Vb0nS5iUsPTcOClnH16nNlAkDrwNCgAmD8tbBUo99GNGjHfKWrISgJ3dkKjp/6z6cq5aHkUV+/PzyXnkGpSGZY4PaoGfFWoKA3HUYNHm+TBg02Xvps52DQvrw0tbeTBi01bPpmxcThvThlZ7ezXoOx7HNy1C3Q1+c8/xH5tBX74NgU9ASukbZEffzW3pUKLQvxwaFwsnCsiACqRIGuiHp0qVLePXqVTJoBg8eLNyapAmNCYMmeEkxNnJQUAyOmi6FCYOme1Ax9jNhUAyOKUkpWO4PvH9wDdNWemDyoO4yFQnF3fT0AMQjLu4XdHV1lGdUKpI5NqgFdrUo/fXrl3B8vmbNmmkWULt165ZQhI2eE2U1KihH16pSMTVTU1OFzImeq9++fYsaNWpkWh7VR6Oq1U2bNoWOTvL/X9euXYOlpSWKFi2aKV0yCQOBPXToUEE4gfR7kbZjx47xkaRMQc+DlYEABwVloKp5MpkwaJ7PlGExEwZloJogU88oO35FR+Lpy3coUaRAqor0s5shLjIc/gFvYGdbSHlGMWFQC7aZUTp+/Hjs2bMHGzZsQO3atRNFSWqB7dq1C5UrV06XCqqcXLZsWRw8eBBlypRJdWy/fv1QoUIF0AU/vzfK3aUixU+ePEn8qFu3bpg4cWKKVZ7TY+TOnTuFo/0098y2Bw8eoHXr1qAq1Pr6+snEde7cWSATbm5umVIlkzC0adMG2bNnx7p165AtW7ZMKVH3YN5hULcHVKefCYPqsBazJiYMYvaO6mxjwqAcrL0v30aTWo4wzl0QP4LfpqkkW97CQj/vK7fR+A+HNPsrowPHBmWgmnmZEsLg6OgIeoim9uLFC7i4uAh/VydhoJ0FIjL0MF6wYEHhLX6fPn0EO8nezDStIgxNmjQRGMmQIUMyg4koxjJhEIUbVGIEBwWVwCx6JUwYRO8ilRjIhEE5MPefvBDrZvwNu1rN4X/xcJpKLGztEfryIdbvOY7e7RIeBFXdODaoGnH59BFhoAt19u7dC09PTzg4OGDy5MkwMDDAli1bEgkDHbuZPXs2Tpw4IZxu6dChg3Ddv+SN+pUrVzBz5kw8f/4cFStWxN27dxN3GH78+IHFixfj0KFDwokZeuPevn17mJiYILUdht9nQLLp2ZhO2Mg6wjRjxgzY2NgIb/Pj4uLg7u4OOsJfrVo14Xg/1TXbvn07Dhw4ILXDsGPHDkHuokWLhONDvzeS9e+//wpjIyIi0KBBA2Gng45T/b7D8Pr1a6H8AaUUkC2fP3/GyJEjlbfDMG/ePNy5c0dwoKY3Jgya7kH57eegID9W2tyTCYM2e1f+uTFhkB+r9PSs0KAt7p3ZD/e/Z2Lz/AlpDi1csSbe+l3B9FVbMGlg5o5FpKlMRgeODQnA+Hn4ITQwVPh7RfeKyGWbSyW/l+U3Igz00Etn7+mBnB646Vz/6dOnhYdiyQ7DX3/9JRy5oVpgX758waxZsxIfgukBuX79+sJOAB0hopph9GAuOZJED9b+/v7CWNIzadIk4SGebgGVhzC8efNGsOPkyZNo3rw5hg0bJnMZUoFjIi/btm0TSAsRE/ohskMkgW4aPXLkiLBLITmStG/fPowdOzaRMKUknJ7F58yZI/TLnz+/QIBo12P16tVShIHGEqkhYkTHuiidgMb8+eefyiMMXl5eGD16tLD9UqBA8vOJ5JTf8xoy+h9Z2eOYMCgbYfHI56AgHl+o0xImDOpEXzy6mTAoxxdmhUsi/O0zeB6/gA4u/507l6WtTB1XPLp4BIOnLcGKycOVY1QaUjk2JAC0pf4WBJ4NFP7uftYdNs42Kvl9WoShe/fuqFWrFurWrSu8YaeHent7e+FBvXTp0sKuwZIlS4QHdmpEGCiZlx6+6aHZw8MD169fFwhB0hwGKg9Qvnx5TJkyRdi9oEZ5A7QeaJw8hIHIBvWlBOl69eph2rRpwvMv6SYSI2n0gP7s2TOBtDx8+BDLly8X/n3jxg1hLNlMuxpjxoxJJAy9evUS8oVp56Bq1apCcjUdg5I0yq9o1KiRQDrs7Owwffp04SMiL4MGDRLkUvK0JIfB19cXXbp0EXZiJEnOSs9hIPZFCmU1Ltymlu88VspBgdeAHAgwYZADpCzQhQmDcpycUhLzQ8+HeHLoCew72aOka0kpxdVbuuP6oS1oP2g8dq+cpRyjODbIhatYdxjoBbUkn4GePentuYQwmJubo3Hjxjh16pRwxIYa7R4QCaC3+FRUOCoqSnjrTi0pYaDbPGksJT8nvdmTSAk90MtDGCTA0rGoOnXqCMd9WrVqBTpGRPolbcKECQIhoIRrIiW0K0JEgxKqiSxIdkWcnZ0FwkBHr6hRzjCd6qEWFBSEhQsXJsp0cnJC27ZtQX/SDkm7du2Ez96/fy/YcvjwYcTGxiYSBiIxZF9Su5ROGORaeRrSiXcYNMRRCjCT3yIpAEQtEMGEQQucqIApMGFQAIi/ifgZHQMTI0NAVw9xMTGJ16SeGX8Gl+dchvN0Z9SeJL3rIKnZUK9TP5zZuVbxRskhkWODHCCpoYvkSBIRBjrjT+fu6SYiIgASwlCsWDFUqVJFuISH3vBTW7ZsmfCGn0jEpk2b4O3tnXiEPilhIOJBCcp0HIge5H9v6SEMNLZhw4bCQzuNk9V69+6N3Llzg16snz17VrCVjjURyZG8bCfCQCSBjgvRbgrtWtDOgKxGOyu0A0O7E9To2FOPHj2ERGzKUZDsMNCOBxEQIgx0cRE1lRIGShihxBJKQtHExoRBE72WMZs5KGQMN20bxYRB2zyasfkwYcgYbqmNuuLrj5oOZWGUyxI/Qz8mdr258iauzLuC6n9VR7UR1aREUK7DloWT4NC4A257qycvkmOD4teCIiQmJQxJ5SUlDHStKh3zoRoIlOMQGhoqXMhDNykR0aCH5BYtWghHluhNPCU3z507NzGHgRKQ6S087UDkyZMHjx8/Fh7ce/bsmeoOA+Xx0nEkIgm0y0FyaTeDdhaIwMhqGzduFPIN6MjQ8OHDheNJtCNRrlw5IW+BWtIcBiI7NB/a8aD8g5QakQ5KDF+xYgWsrKyEXQTK1SAiRPIlhIHGEkEi0kXzvnfvnrAzodSkZwKXkjcocYPOVNEWCSWIEHMyMjIStlk0pTFh0BRPZd5ODgqZx1AbJDBh0AYvZn4OTBgyj+HvEv7dcwx/dmgGuvnoS8D9xI8/+X1CzkI5YZLbJJnSv+etwcKxA1HMqQGeXz+leKPkkMixQQ6Q1NCFCAM9jNNDbWqEISAgQHgAp8QvK6ciAAAgAElEQVRoarTTQM+ldGMSFWqj4z9Hjx5N/Ize7NMDPp37pyLE9Bb//PnziSromBAlRlPeAeUJ0I1Lvzc/Pz8hWZiegSWN7CWikVqTEATJrgbdAkU1JugZetSoUcJQys2gZGdJHQbKYSASIMll+F0+1YSg3QVJqgAdzaJn9OLFiycSEkkdBnpupx0LaqVKlQIdpaIdESIRmWky6zAQ2KSAEi0okYSywmmyEibEOQyZgZ3HKgsBDgrKQlaz5DJh0Cx/KctaJgyKR3b0/LVYMGYAilatjxc3TicoiAem6U0T/pwUPQm6BrpSihdv3ouRPdvDsrQjPj66pXij5JDIsUEOkDSgC/mRchHMzMySWRscHCychEnpM+pM3wcUG2iXIaUCZylNnx72aUeDajLQ7UTqPmlDD/906od2GVJrRDDo+tW0+qXH5TIJA+0kWFtbC0kllMVNZIF+iKnROSp5KuilxxBl9uUdBmWiKy7ZHBTE5Q91WcOEQV3Ii0svEwbF+6PjkEnYvXImqrl2w7VDWwUFP0J+YH7u+cLf+/v1h2V56Xvk6TalTk3rIkfBEgh/+1TxRskhkWODHCBxF0YgFQRkEgYiBXRTEp0bS4kwHD9+XNgK0YTGhEETvKQYGzkoKAZHTZfChEHTPagY+5kwKAbHpFJqtumJK16b0brfaOxfm3CzS/ibcGxtvBVhr8Kgo6uDcRHjpBRf9XuMGhXtYGiWD1FfPyneKDkkcmyQAyTuwghkhDAQWaCgS5X2qBaDZIeBkkokhSIol0ETmtgIQ1R4FHzW+MAopxEcB/xXXryeRz34fvTF86HPkdsktyZAKzobOSiIziVqMYgJg1pgF51SJgyKd0mpGk3w9Ko3hs9cjiUThkgpmJVtFmJ/xGL8t/EwyP7fJSkfgkNRIK8FdAyM8Sv6h+KNkkMixwY5QOIujEBGCANlkbu6ugp33oaHh6NSpUpCmWtKGqGkjdSulBIb4mIjDDdX3MSVBVeEtzIN5jZAjTE1BMic1jvh5rubuNr7KqoXqi42GDXCHg4KGuEmpRvJhEHpEGuEAiYMindT3pKVEPzsLpZvO4AhXVtKKVhtv1rIY+h+pjtMrUylPtPRobyGeERE/oCpibHiDUtDIscGlUPOCrUMAZlHkmieRBpoR4Gq1FECBWVbUzU+uoNWV1c6qUnMuIiNMBBWTw4+gVd3L+GLtYBjQiVtNy83bLu3DZtbbYZ7BXcxQypa2zgoiNY1KjWMCYNK4RatMiYMineNSZ6C+PnlPU5f90V9p4qCgoh3EYiNioV5UXOZCg1yWCD2WyjuPQ1EuRIJxbdU2Tg2qBJt1qWNCKRKGJJOmDLFqeS2JjYxEgbCccf1HfD+4A23Cm5oWLQhZlycgcnnJmN8rfGYVU891TA10b9JbeagoOkeVIz9TBgUg6OmS2HCoHgP6hoYIz42CsFfw5HbLIeg4PyU87gw/QLqTq2LOlPqpKjU1MoW3z+9xMGz19DCWbpOg+KtTC6RY4MqUGYd2oyATMJAd93SUSRZrXz58nJfS6VuAMVGGIIeBMHU0hTDrg/D+jvrsdp5Ndzt3HE46DA67e2EdmXaYU/7PeqGTSP1c1DQSLcp3GgmDAqHVCMFMmFQrNtevQ+CTUFL6BplQ9zP74nCj/Q/gtvrbqPJiiYo0bQE9I30kaNgApmQNMtSlRH09A6WbN6L4e5tFWuYHNI4NsgBEndhBFJBQCZhoGIWZ86ckTmU6zBkfF1NzzkL8RGx8BjuiUL386D+mfpovLgxjDobwWGdA8pblodff7+MK8jCIzkoZGHnJ5k6EwZeB4QAEwbFroPjF33QtE4VZLe0wbePgYnCT489Db8tfrCpbYMHng9Qe2JtOM9wllIuSZb+a/YKLBo3WLGGySGNY4McIHEXRiAjhOH9+/dC3sLvjSrNUX2GRYsWQU9PTyPAFdUOQzwwVXcqdKCD6WNmoppvVTQ62QhVBlVB/WX1YTzTGEZ6Rvg58adGYCs2IzkoiM0j6rGHCYN6cBebViYMivXIok17MKpXB5kF2Oj2v6MDj8KhrwNc/3GVUl69pTuuH9qCtgPGYu/qOYo1TA5pHBvkAIm7MAIZIQyyxly4cEG4ZvXOnTtCSW5NaGIiDHSl6t95xkE/FlgyZQnKPiuLHqd7oHzX8mi0qBEKLi6I9xHv8XrEaxTOWVgT4BWVjRwUROUOtRnDhEFt0ItKMRMGxbpjwJRFWDt9FOxqN4f/hcNSwh8HP8bDfQ8RMCUAldwrocG8BlKft+k/Bl7r5uOPVu5CHQdVN44Nqkac9WkbAnInPUsm/urVKzRo0ACenp5wcHDQCDzERBgIMD2j7PiVLxLoA1jqWOPj5FeJOLpsc0FAaAD2dNiDCpYVNAJfMRnJQUFM3lCfLUwY1Ie9mDQzYVCsN1x7/4UjG5fAueOfOLtrXaLwj3c/4i+/v7Dz5U6saroKA6sMTKZ4yPSlWDllBOho0uPLxxRrmBzSODbIARJ3YQRSQUAmYQgKCsKPH9IFViIiIrB161bs378fnMOQsXX18Pkr2NOVcsQFWgNmQYXwddWbjAnjUckQ4KDAi4IQYMLA64AQYMKg2HVQuUkn3PH2RLcRU7F18RRB+K+YX5hhOAO/9H5h+qTpmF1/NsbVlK70TP3SOs6kWEuTS+PYoGyEWb62I5DupOfs2bNj2LBh6Nmzp8ZgI6YdhtXrvLCg/1/4Vu0bgl2CoXvFGFEHIxAaEAozazMY5jDUGFzFaCgHBTF6RfU2MWFQPeZi1MiEQbFeKeJQB699L2LS0g2YPqyXIDzsdRiWFlmKnxY/MXfoXBx0PYiqulVhVdFKSvmBM9fQusEfyRKmFWuhbGkcG1SFNOvRVgRkEgYq2hYSEiI1byILZcuW1ZjrVCXGi4kwjGg4G7lOx+B2xXs43Go/sBdYbLQC4be/oNuJbijWqJi2rjWVzIuDgkpgFr0SJgyid5FKDGTCoFiYcxWxQ9jrx9h66DS6udYXhH95+gW7Wu7C45jHWOm2ErMWz0JMeAxGB4+GSW6TRAOevHyH0raFoGeSA7GRsq9sV6zF/0nj2KAsZFluVkEg3TkMmgiMmAhDz9J/weaJGd5Xi8LZ3Dfw/MxZDCs2GeYPddFsdTM4DnDURIhFYzMHBdG4Qq2GMGFQK/yiUc6EQbGuMMyZGzERIfB5+AyVyxSXEl50WVEEfg3Eku1LEPYsDAMfDETesnkT+/z6Ff//mxXj8f1HFLIZq3Y3nWODYtcCS8t6CEgRBqq78Pr1a7lQ6NKlC4yMjOTqq+5OYiIMzQp1Q753BijSvyoiisRj8bhBcLHqjEb6VVBrfC0mDJlcLBwUMgmglgxnwqAljszkNJgwZBLAJMP/e+AH4uLioKurIyU89/zcCPkRgo2XN+L76+9o59ku2bEkw5x5EBPxBTfuPUHVciUVZ5wckjg2yAESd2EEUkFAijAMHToUx48flwswTnqWC6ZknajgTWTQKxy/5INihfOjpE1B6Ogb4ldMVMYE8igpBDgo8IIgBJgw8DogBJgwKG4d+D0JQMXSxWCQwwLR4V8SBX99+RX0sz14O2LyxGBq3akylaZ0pElxFqYuiWODqpBmPdqKgBRhePr0qTDPkiVVy/yVDa6Ydhh0dBLeysTHxwt/5shfVKiYmfRMqLLx0Gb5HBS02bvyz40Jg/xYaXNPJgyK8+72o2fRrXl90EN/6Ev/RMEnRpzA9aXX4bLMBU5DnVJVaF2xFt74Xca0lR6YPKi74oyTQxLHBjlA4i6MQCoISBGGXr16oVSpUqBqzvPnz4etrS3at2+v8QCKhTBc9HmAblWawiRnPjwJ8xFwpQI4jy8dRa9hszCrR69kW7gaD76KJ8BBQcWAi1QdEwaROkbFZjFhUBzgU5ZvEm5Gsq5UG6/uXEgUvL/rftzfcR9td7aFfSd7RLyPEBKhcxbKCYviFlIGVKjfBvfOeqHXmNnYMDf51auKsza5JI4NykSXZWcFBJIdSTI0NMTChQvRv39/2NvbY/DgwRqPg1gIw9x/dyHyT3/oQQ+ToidB10AXnYZOgueKmRhjNAEmUQYY8WaE8EX7JvwNomKjUNxCOrFM452h5AlwUFAywBoingmDhjhKyWYyYVAcwD1Gz4LHgolwcOmI28d3JQo+NfoU3t96j0aLGiG/Q37c334f+7vtR3m38mi9pbWUAQ26DsSZHWvQxH0ojm1epjjj5JDEsUEOkLgLI5AKAsmSnokouLq64v79+8iTJw/++OOPFIf37dsXxsbGGgGuWAhDl/6TUGqdPmIMfmFm9DQBuzU7D2NglxboazQIBaPyosf5Hrif/z5cd7qicbHG8O7mrREYi8VIDgpi8YR67WDCoF78xaKdCYPiPFG3Q19c2LMezXoOF6o9y2qBZwKxpcEWFG1YFG4n3aS6dR81HVsXTUGlRu1x58RuxRknhySODXKAxF0YAXkJQ0xMDA4cOABvb29cvHhRGGZhIb2lKJF16tQp5MyZUyPAFQthqFHPDSXP6SNPGVsseDhZwO5LWATy5MqJljqt4WxTF83XNEOcYxxKrSyFYubF8Hzoc43AWCxGclAQiyfUawcTBvXiLxbtTBgU54kS1Rvh+fVTGD5zOZZMGJKi4Hoe9RDoH4gJ1yfAspQlXP9xleo3YckGzP6rD6gA3Mvb5xVnnBySODbIARJ3YQTkJQxJ+9GxJMphaNu2rcYDKBbCYGVXBZ8e+2Cpxz4M694mEddseQvjR/BbeJ2+ilb1qyMuPg760/WFz2Mnx0JPR0/jfaCqCXBQUBXS4tbDhEHc/lGVdUwYFIe0uU0ZfH31CBv2eaNXm8aJgl+efwldPV1Y17KG7TJbvPz6UnjRRS+8fm8b9nqjT/smIFkhgQ8VZ5wckjg2yAESd2EEMkIYtAk1sRAGo1yWiA4LgvVCW+QwyYYLPS8gt0lulKjeGM+vn8SgqYuxcsoIAfriy4vjRegL+A/yh10eO21yh1LnwkFBqfBqjHAmDBrjKqUayoRBcfDqZzdDXGQ4Hjx7ibLFiwiCvwd9x0LLhcieLztGfRoFh3UO8P3oizv97qCSVaVkyi/feYhale1hmCsfokI/Kc44OSRxbJADJO7CCGSEMNAX7blz50DF3AICApKJ2LJlC0xNTTUCXDEQhtcfPqNcgeLIrWeJ8MEh+GL+BdGTomGga4C2A8Zi/9p5qFe/NxZ2GYRKvSqh8bbGOPniJLw6eqFV6VYagbMYjOSgIAYvqN8GJgzq94EYLGDCoBgvfAoJg1XuXNAxMMKv6J+JQoMeBGFNuTXIZ58PA+4PQN3NdXHh1QUcsD+ACrkrwKaujZQBoeHfYWGW8NwguVpcMRamLYVjQ9oYcQ9GIDUEpJKek3Zcv3495s2bB0dHR1hbW8PAwEBKzoQJE2BiYqIR6IqBMMxetx2n+q9HXdTFOedzuNvoLr6O/Srgt2zLfgx3b4vxuhNh+EsfwwKGYeKjiVh1axXmNZiH0TVGawTOYjCSg4IYvKB+G5gwqN8HYrCACYNivHDo3HW0rFcdVHiU6gYlbUf6HcHrwNf4POEzdtzfgadfnmLWqlmI+RyDkR9GwtRK+sWinnF2/IqKxKv3QbDOn1cxBsohhWODHCBxF0YgFQRkEoZ69eqhWrVqmD17tsYDKAbC4Ni0M6yOR6AKquCw62GENwzH0yEJhfLefPoCa6s86KrjhhLxxdBuVzuctjmN4d7D0btSb6xvsV7jfaCqCXBQUBXS4tbDhEHc/lGVdUwYFIO0pAZDofJ/4I3fFSmhMZExmHlxJqbfmJ74+5kbZyL2dSyGPh8K82LmUv0lOXvHL/nApWZlxRgohxSODXKAxF0YgYwQBirY5uTkhFGjRmk8gGIgDIZmefFHeFnUsWsAj+rrUbhmYVzqeSkRW2NzK1T7Whoty7dF8zkueFbiGZrtaIZa1rVwsWfCjVXc0kaAg0LaGGWFHkwYsoKX054jE4a0MZKnR8chk7B75UzQi69bR3eAdhWsKlnBsb+jMJxebi278V9dhdl3ZqNAeAG0WN8iGWHIW7ISgp/dxfKtXhjSTXXHbTk2yONp7sMIyEZA5g7Dtm3bsHHjRhw/fhxGRkYajaG6CcOpa75o9IcDdI2yY4fPBnTa1wlt7dpib4e9ibjaOtbDy9vn8Pe8NZg/ur+wrdt8R3O0tmstHEviJh8CHBTkw0nbezFh0HYPyzc/Jgzy4ZRWr6rNuuLWsR1oP2g8tsyYhHkW86BnqIcJPyZAR1cHvQ/1xkbfjSiUsxDehr/FdOfpmFR7UopiJdez/jV7BRaNU11hWI4NaXmZP2cEUkdAJmFYuXIlli1bhooVKyJv3uTnDBcsWIDs2bNrBL7qJgySas7FqzXC0GXNMfT4UAxwHIDVzVYn4te81wgc3bQUddr3wfnd/2oErmI0koOCGL2iepuYMKgeczFqZMKgGK8UrlgTb/2uYNLSDehVoR48nD1gVdEK/Xz7CQra72mPvf57Ub1QdVx7ew0jq4/EwkYLU1RezbUbbhzZLpCP3StnKcZAOaRwbJADJO7CCKSCQKqEwc/PT+bQpUuXMmGQc2kVsK+GDw9v4O8m82Dyx3dMj5uOaXWnYXKdhOJt1Gat2YaJA91gW6Y2js1cB8sKljAvKn32U051WbobB4Us7f7EyTNh4HVACDBhUMw6MLWyxfdPL7H35CU0KlsBflv8hB2G6n9VFxS4bHPBiRcn4NHKAzWsa8Dsoxm+ffqGvGXyCleuJm2t+43GgX8WoFbbXri4d4NiDJRDCscGOUDiLoxARgiDNqGmzh2Gbz9+IkeOHDCI08EETEC8XjymTZqGNc3WoL9j/0SYj1/0QdM6VdDWqAvKRZVEk+VNUHVIVW1yg0rmwkFBJTCLXgkTBtG7SCUGMmFQDMy6BkaIj43G14jvMDPNlkxo9Q3Vcf3tdVzrfQ3VClXDod6H4LvRFy02tBCuCU/aqN7Q6mkjUbpWMzy6eEQxBsohhWODHCBxF0aACUMJPHv2TC0LQbJzUMSqMnp+dEUu21xo6tMUZkZmyJc9X6JNYd8ikStHdjigMlrAFeW6lkObbf9Vg1aL8RqolIOCBjpNCSYzYVACqBookglD5p328Pkr2JewgZ5JDsRGhqco0H61PR5+fphYaNR7mDduLL8Bl2UucBrqJDVm/npPjOnbCVZ2VfDB/2bmDZRTAscGOYHiboyADASkjiT5+PgI16iuWrUKXl5eSO1I0uLFi/lIkhzLqkqzLvA5thOuLoPQPs5J2J6VRQQMclgg5zcdzG28EOUa26HaiGpyaOAuSRHgoMDrgRBgwsDrgBBgwpD5dbDZ6yR6tmkMs8Kl8PX1Y+xutxs5CuQQdsElrfCSwkKy85sRb4TEZ98Nvri37R4c+jqgXJdyUkYcPn8DLZwTYpsqi7dxbMj8WmAJWRsBKcJw+/ZtzJ07F8uXL8eBAwdSJQyLFi1iwiDH2qHrVGPCg7Fm12H079g81REWtmUR+tIfG/Z5o1ebxnJI5y6/I8BBgdcEEwZeAxIEmDBkfi2MmLUCSycORdGq9eG7bz+WFF4C0/ymGPl+ZKLw+VfmIzImEmNqjoGJfuoFXYVjutkS+tDOes7sqikAy7Eh82uBJWRtBGQmPWsTLOrKYZBcp6pjYIyf3yNgaKCfKqwlqjfG8+snMXzmciyZMESbXKCyuXBQUBnUolbEOwyido/KjGPCkHmom/UcjmOblwlJyht6jcWOZjtQrFExdDvRLcPCTfIUxM8v7+F5/AI6uNTOsJz0DOTYkB60uC8jkBwBJgxKXBWSYjflyjfF7GoDUKpFKZRoVkKmRueOfwpXqrZtPxJD7F1hammKyv1UVwlTiVCoTDQHBZVBLWpFTBhE7R6VGceEIfNQl6vXCg/OHYT73zOxYthQBJwJEI7WFncpLlP496Dv+Oz/WYhheezyJOtnXak23ty9pNKXYxwbMr8WWELWRkCjCAPlV2zatEnKYw4ODvD09Ez9zX0J9SQ95y/rhI/+NzGs+WyYH4kWCEOng51k2tpn3FxsmDsOjcq74497trCuaY0CmwsIRdx6VuwJU0PTrL1a5Zg9BwU5QMoCXZgwZAEnyzFFJgxygJRGF8tSlRH09A7m/bsLo/t0lEvgo32PhFwHu7Z26LC3Q7IxVG+IrlSt32UATm//rx6RXMIz2IljQwaB42GMwP8R0CjCMGvWLLx+/Rrjxo1LdKCxsTGsrKxERxi+hEUgT24LIC4WG9ttw+u9z9FwfkP88fcfMm1dtGkPRvXqAJsSNdDjWUPom+hj4+yNCAgLwLMhz1DcQvYbHV7RCQhwUOCVQAgwYeB1QAgwYcj8OjAyt0T01yCcvHoHDatLX5EqS/qLEy+wzWUbijUuhm7eyY8uSa5Wta1SDwE3z2TeSDkkcGyQAyTuwgikgoDGEYbQ0FAsXJhyBUlZ81RHDoPkOlXT/EVxb9tpPNr/CA59HITqmLLa+Vv34Vy1PIxzF8De9v/AsrwlBuoPxK33t3Cjzw1ULch1GdL638xBIS2EssbnTBiyhp/TmiUThrQQSvtzCWGgG408W3vCzNpMuC5VVmvj2QYPbzzEiJsjYFPZJsW+24+eRbfm9ZHdyhbfPgSkbYQCenBsUACILCJLI6BxhMHb2xvVq1eHubk5GjRogCpVqqTpQHUQBsemnXH7+K50V7PU0dER5iO5bk5SQfNY12NoUvy/a+zSnHQW7cBBIYs6/rdpM2HgdUAIMGHI3Dqgys7tG9eGsUV+vLl8D6vKrIJFCQsMeSr7Ug6HdQ7w/eiLW31vwbGAY4oGfA4NRz4LM6lYlzlL0x7NsSFtjLgHI5AaAjIJw8SJE1GyZEl0795davzjx4/Rt29fHDx4EBYWFipFl3QGBgbCyMgI9+/fx6lTp4QrYJs0+e9B+syZ5Nub/fv3x71791Rqq5mVtXCd6oL1uzCoSwtB95KbS7D2zlqMrj4avSv0TtEeybiLt+7BsWxx9DzcE56PPLHJdRM62sl3flSlExWZsvDwcOTMmVNkVrE5qkYgMjIShoaG0NdP/WYyVdvF+lSLQExMDGJjY2FiopqrO1U7O+Vr6z1uLnYumw66wc9zwBwc6n4IpVqXQsvtLWUqb7KrCS68voDjnY6jjnUdmf0kse7wueuo71Re6ZPRltjAa1npS4UVyEBAJmGgh2x7e3sMHjxYamhQUBBq1KghEIYyZcqoFdhRo0aBjiht2LAh0Y45c+Yks2njxo2gonSqamdu3kPbRrVRSa8KxnUbDbMCOVG6Q2nMeT0Ha33XYlbtWRjkMChFc4o61EJIwH0sXLkTtncMcePNDcyvMR9z6szBgEoDVDUFjdVDbxQpr4Vb1kYgOjoaenp6wo+qmq6uLn79+qUqdaxHDgTi4uJAP0QeuaUfAUeXDsJV3x0HT8DCfv3x9vJbmBY0ReHahWUK63q4K46+OIp/8v+DWja1kN8pf4p9S1VviE+PbmHM/DUY92fn9BuXzhHaEhvMzBJ2ZrgxAqpGIBlh8Pf3BwVbevAuVqwYOnT474YDelNz9OhRbNu2TXhjr26mS7kMVGxu586dqeKm6iNJPUbPgseCiehvOhxW33IJtrmddMOE8AnY9WAXtrXZhq7luqZoc9m6LeF/4RCGTV+OvHPCEfsjFnPGz8GYhmMwre40Va8PjdPH284a5zKlGMxHkpQCq8YJ5SNJmXMZHUWKCv2IPScuol2jWikKu/nuJo4/P45qhaqhcbHG6HGgB3b47MCEWROEizsmRE5IcVz1Fm64fngbWv35N7zWzc+coXKM5tggB0jchRFIBYFkhMHJyQkhISEyh9AxpH79+qFXr14qB5YIQqtWrVCkSBHQ0Sg3Nzf8+eefGDhwoKgIQ6kaTfD0qjcGu85EzxZNhDur6UrVeh71cO7lOZx0O4mGRRumaHPDboOEa+Yauw1Gx0AHvL78Gtu6bUPjzo2xsulKlWOuaQo5KGiax5RjLxMG5eCqaVKZMGTcYy/efERx6/zQ0TdE9M8f0NfTTVHYnMtzMP7MeIytORZz6s/BMO9hWH5jOaZOnwr8AibHTYaObkJuXtLWc8xsbJ4/AXa1m8P/wuGMGyrnSI4NcgLF3RgBGQgkIwzPnz8HnfucPn066M18587/bRUaGBigaNGioK13dbQ2bdoIuQuSRv+eNm1amkdQVLnDEPM9BtXyOOPpTz/sOHcarnWdEu0tu7os/D/7w6+/H8pbpnxms//khVg342+UqdMChycswYWPF9AroBc62XfCzrap76Sowydi08lBQTUe2XZvG9y83OBa0hWHOh9SjdJ0aGHCkA6wtLgrE4aMO3f84n8xZ+SfyFeqMj499oFXdy8Y5TRCw3kNYZDdIFHwuDPjMPfyXMyuPxvjao7D5HOTMePiDEzxngJbY1t0PdZV2Gn4va31PIIBnVyRq4gdQl/6Z9xQOUdybJATKO7GCMhLGCT9KGmQzv9SgrGYWkREhLADki9fPrmPRKmSMBxafAa+Iy8jUOcVNv/aKAVd3gV5ERwZjI+jPsIyu2WKsK7YfhBDu7VCnhIV8fmpr3Cl6uJri4UdiV6VVL+rIybfy2MLBwV5UMp8nz6H+mCD7wYsaLgAo/4YlXmBCpbAhEHBgGqoOCYMGXdc9ZbuuH5oi1Bc7cTGFZhpPBM6ejqYHDtZSuigY4Ow+tZqYQd8UJVBWHRtEUadHIXh1YZjSeMlMg148vIdStsWgq6hCeKiIjNuqJwjOTbICRR3YwTSSxio/8ePH3Hp0iW8evUq2XBKhtaU5FJVEoaJFefCwC8KVws8wol3uxJxi0c89KYlJGDGTYmDDpJv0dJnV3z9UdOhLAzN8iHq6ydeuOlEgINCOgHLYPcSK0rgechz3Ox7E1UKpH21cQbVZHgYE4YMQ6dVA5kwZNyd5jZl8fWVP+b8sxM9qzpjbZOvh7IAACAASURBVMW1yFsmLwY+lD4CTDuNtOO4pfUWuJV3w/o769H3cF/0rNgTG1tKvzT73Rr97GaIiwyHz8NnqFxGuYVJOTZkfC3wSEaAEJB5S9Lx48cxdOhQASXKW/j9loljx44hR44cGoGiKglDM8e+MLj9GfpdSmDv9gWJ+Hz89hH5F+UXdhZoh0FWi46JhZFhwnZvVHQMLk48j+Anweh0oJNGYK1uIzkoKN8DkrVsamiKz4M/Y9MfmxATGYMmK5qgRNMSyjdADg1MGOQAKQt0YcKQMSeHfYtELrOcwK84vP8cgvx5zPHtwzf8DPuJPKXzSAltuaslDj05BK+OXmhVuhWCvgchLCoMOV7lEPrnd8gvHGXqdbAXjPWNMav+LJgbmwsy8paoiODnfli0aQ/+6tEuY8bKOYpjg5xAcTdGQAYCMgkD5Qdkz54d69atQ7Zs2TQaQFUShpyFSiLi3TP8s/so+rZvmojbvU/3UHNjTZTJWwbX+1xPFU+q9BwV8gFX7j7CrdYn8DXwK4a+GArzoglfstxkI8BBQfmrY+eDneiyrwtcirvgeNfjwtnme1vvoebYmqg/p77yDZBDAxMGOUDKAl2YMGTMyRv2eqNP+yYwtbJFRBqVmOturosLry7gTPczqGdbL1Ghh7MHXp5/Cfdz7oguF41SK0vBytQK70e+T9xhlxQ4rdG6By7v35QxY+UcxbFBTqC4GyOQXsJAxdCaNm2KIUNkV3TUFFRVRRg+hYTBKnfCNapfI77DzDQ50Xr59SVsctmkCp2VXRUhyWz5Vi+YbQpH4NlA9DjfA0XqFNEUyNVmJwcF5UM/4OgArPVZi3kN5mF0jdH4/uk7osKjhAqwYmlMGMTiCfXawYQhY/i36T9GuOq0QoO2uHtqb6pCHP9xxO0Pt+Hzpw8q56+c2Hdni514evgpuhzpgt05d2Pi2YlCjkPS2/4OnbuOlvWqCzcxPXoWgFI2BTNmsByjODbIARJ3YQRSQUDmDsO8efNw584deHp6ajyAqiAM0d+iMa/GChy+54kXRX/iy4uMV5au3KQT7nh7otuIqVjcZyC+B31HPvt8yJZHs3d6VLGQOCgoH2W7VXb4/PQzdnfejXrV/nujqHzN8mtgwiA/VtrckwlDxrxbtEp9BPqcxcApi7Bq6l/Y1WoXPvt/RpttbVCwqvRDffHlxfEi9AWeDH6CkrlLJiq8OOOi8LKrzpQ6aPW4Ffw++eF099Oobyu9C1mieiM8v34Kf7RyxxWvzRkzWI5RHBvkAIm7MAIZIQxeXl4YPXo0+vTpgwIFCiQT0bFjR42pnqkKwnB0wFH4rPXBW7zFk7a/cHHvf9Wn07sCJfdTl6/fBn6n96V3eJbuz0FBue4P+RGC3PNzo92BdrC/a4+2O9vCvpO9oPTHlx/QNdAVziuruzFhULcHxKGfCUPG/GCUyxLRYUE4ePYaWjhXw4qSKxDyLERIeKbE56TtVMApPPr8CD0q9kBOo5zJFL4KewWbpTawMLFA0N9B0NORrr5+4uoduNSsAh09fdx/9BRliytnJ51jQ8bWAo9iBCQIyNxhoFuQTpw4IRMpHx8faEqJcmUThqiwKKwsvRJhn8KxKn4lhq1cgsmDumd4lW3cfwK927ogV5EyCH35MMNysuJADgrK9/oNvxs4UfkE4uPi0d+vPyzLW+Lwn4dx59876Li/I0q3Lq18I9LQwIRB7S4QhQFMGNLvhjefvsDaKg909I0Q/TNSKNg2XX+68P99wo8J0DdOXlMhNS3zrszD2NNjU701qXTNpnhy5TiquXbDtUNb02+0HCM4NsgBEndhBFJBQCZh0CbUlE0YCKtnT96iUelaeImXCHjzEbaFUq6zIA+uklwIHQMjvL/5Ens77kUum1zodqKbPMOzdB8OCsp3f2hAqJDobJDNAG4n3UB5OV5DvRC+NRy1J9WG83Rn5RvBhEHtGGuCAUwY0u+lBRt2Y3SfjshdrLxwgxG1HyE/EP4mHJYV5I9rYa/CEBoYCvfr7rgYdRFHuhxBsxLNUjTo7E0/1HeqKHz29tMXFMyn+Hwojg3pXws8ghFIikCqhCE+Ph6BgYH48OEDihUrBisrK6EmA92alDev9LakmGFVBWFw/3smtiychHwlHfDpye1MwyG5KenInsvwaX8aegX14HLTBY4FHDMtW5sFcFBQnXcjP0ciW95s2H5/O+ZPm482+9rAaZgTXJa6qM4IGZp4h0HtLhCFAUwY0u+G5r1G4OimpajavCtuHN6WfgH/H3F1wVWcGn0KV2pcweUml/F17FcY6hnKlGdZqjKCnt5JzJvIsGIZAzk2KBpRlpfVEJBJGL59+4a+ffuCjh5RW7hwIVq2bImBAwcKJILqNGhKUyZheLDzAagGW6Px7vgQeAf9Ji3A2umZr3xrU9kZr+6cx8RFG6A/8g1+6f6C0TkjjK89XlNgV4udHBQUD3vA6QD4efjBoriF8FOiWQkY5zJOVLTx3F4MOuWGshZl4TMq4ftC3Y0Jg7o9IA79TBjS7wdbx3p4efschk5fhmWTEmoxZaT5rPHB0YFH4evki+yjs2N7m+2pivlzwnz8O3sMrMpUxYeHNzKiMtUxHBsUDikLzGIIyCQMu3btwpIlSzB+/Hh4eHjA3d1dIAw3btxAt27dcPnyZVhayr89qU5clUkY5prNFa6UXIAF+I7vCttObeQ2GKe2rUJjt8Fwrl8I0x5Nw4B6A7Co0SJ1Qil63RwUFO+i0BehuDD9Avy2JBxPaLy4MaqNqCb8/UtYBPIXsUHMiBDoQg/Rk6OEpMZLry/hbOBZOBV0Euo1qLoxYVA14uLUx4Qh/X4xMrdE9NcgHL/kA5ealXFh2gX4bvRF3Wl1UbFHwrEhedqLky9wZsYZHDE/gu5zu6NdmYTCbFv8tuDYs2MYXm04qhVK+B6h9vrDZxQpkE/4++PAtwq/YpVjgzxe4z6MgGwEZBKG5s2bg2oxDBo0CL169RLIAv2EhITAyckJ+/btQ/ny5TUCW2URBro1gm6PiDb5hdk/pqOYUwPherjf26PgR0JlSypaI28bv/hfzBn5J4o41MXk9W7ofai3cAvFppbKLW4jr31i7cdBQTmeeXzgMe7vuI8vT78I55lHvB4hKJIQW9CLSAvAp4kP7Gzt0OdiH1CBtz3t9yQ+KCjHspSlMmFQJdri1cWEIX2+eRT4FmWKFpZKeN7XZR9oJ72VRytU6F4hfQIpJyH8LXIZ5wJVhqdG9RhmXZqFaXWnYXKdyVLyijjUwWvfi+g6Yiq2LZ6Sbl2pDeDYoFA4WVgWREAmYSCyQNWe6VhSUsLw/PlzgUicO3cOhQoV0gjIlEUYIoMjcXfzXUyePAdnfhzBtJUeKd6OVGx5MQSEBiS7pzo18M7cuIsG1SrB2CI/dl5ZjdaereFa0hWHOh/SCMzVZSQHBdUhTw8XZUsWQ3xsNNAZcAl0QbXr1dBoUSN01OmIN+FvcG/APZTLV051Rv1fExMGlUMuSoVMGNLnlrn/7sK4PztLJTyvr7Ye7268Q6/LvVC4RuH0CUyh99FnR9F8R/PESvFJu4yevxYLxgyAuU1ZhAQ+yLSupAI4NigUThaWBRGQSRimTp2KixcvYseOHcKxJNpdaNiwIUaMGIG7d+/i6tWr0NOTvk9ZrPgpizDQfHd7X0THJnWga2yKH+GhMDSQvnIuOi4aRjONhGMaUZMSjmvI23T0DYC4WGyc4IH7K+/iU+NP2O6Z+jlQeWVraz8OCor1bHREtHAjUr5y+ZLdfkRVYO+d2S88XMTVCIdNeC60OtAKJTqVQNfSXZHDMAfCx4Ur1iA5pTFhkBMoLe/GhCF9Dm7WcziObV6Gai2649pBD2FwzPcY4bYj86Lmws1o8jYqZvre5z2Mchghf+X8icM+ff8Eq4VWwq57yJgQKXFh3yJhbm4uvITwefgMlcsUl1ddmv04NqQJEXdgBFJFQCZhoKNHRBI+fvwoCChcuLBwHOn79+9Yu3Yt6teXrtYoZpyVSRgqdW2Ju/cOwdG6M24d3ZEMBqpuWXFtRdjns8f9AffTBRO9Zfn6yh/T3Zbg19YwPK3zFNvPM2FIDUQOCulaYml2fnPlDTbW3Ih4u3j0vdEXBXMkVHm97f8cjmVLCH/fduQMdtzbijuvveG+yR1WDa0wwnFEim8Q01SooA5MGBQEpIaLYcKQPgdKEp6Hz1yOJROGpDmYjsoa6xtjboO5wguCpO3zw89Ybb8aecvmxcAHA6U+o+KPVATy/cj3yG/6H5mgTmXquOLRxSOZvqXpd+M5NqTpTu7ACGSMMNCoHz9+gJKf79+/j4iICNja2qJdu3YoWfK/8u+agK+yCMPBXgcx03cufFpdx2DbSVjRfboUHOvvrMfU81PxLuIdOpTtAM92numCq2LDdkKl576tJqDgAQMElA2Ax4OEtz7cUkaAg4JiV8at1bdwbNAx3K58G5ZTLPGv67+gwk6ly1dGZNArFK1aHy9unIb3w/NostcZ+AD0bNQXm+7/ixnOMzCx9kTFGiSnNCYMcgKl5d2YMKTPwZIKzyev3kEd+7K4PO8y6s2sl6KQ8KhwmM01E6o7h40NS9aH6jAstVkKsyJmGP5yuNTnzh7OOP/yPE50O4FGxRpJfbZm52EM7NJC+N3dxy9QoVTR9E1CRm+ODQqBkYVkYQRk7jDExsYiMjJSqLmgr//fMRsiDvRvExMTjYFNGYTha+BXLCu6DD8Nf2Lu+Lk43vW41G0wsb9ihZ2Fh58TKjVPqTMFU+tOTRdmXYdPxY5l01CtQRe8KH0KYbnCEDUjKl0yslpnDgqK9Tgl9o9dMhbe0d6YMWQG2pfqjCLlnRD87C6y5bPG0/u+QpGlb9HfkHdIEfx8EYJ8tQsjKP4NTnc/jfq26tmJZMKg2HWgqdKYMMjvuaQJz79ifmJ32914tP8RKvWuhBbrEx7gkzbv595osr2J8MBPD/6/N7o9cFfLXcIVzB29Okp9PPjYYKy6tQoLGi7AqD+SX0NeuEINvL13FaVrNRN2GxTRODYoAkWWkZURkEkYNm7ciDlz5uDs2bPCcSRJoyTooKAgHDx4UGNwUwZh8N/rL9wAE2AdgC29tuDV8FewNrOWwuTOhztw/McR8YjHrna70LGs9JdmWgCu9TyCAZ1ckad4BTjPLincsrS8yfK0hmXpzzkoKN79pVaWwtMvT1HuUTMEXvDHt4+BMMhhgWvXb0idMe47fh7WLxgLy75WcLpZFZ7XPYXjCupoTBjUgbr4dDJhkN8n/+45hv5uHZCnSCkc++sfHOl/BIamhujn20+ov/J7o93zaRcSbjqiG4/S09b6rMWAowPQvUJ3eLRKvmsuufSDZO44cg6dm9VNj/gU+3JsyDSELCCLIyCTMLi5uQlHkKZPlz5mQwnP7du3x6VLl4TKz5rQlEEYXt//gB5V3BHm8gX3K99H9KToFKHY/2g/CuUshKoFq6YbKjr6YW2VB7pG2RH381u6x2fFARwUFOt1SdI+4gHMBBAH6BplwwHvs3Ct6ySl7MnLdyhdvRB66/VG4XeFhbeS9HZSHY0JgzpQF59OJgzy+6R6S3dcP7QFDbsNwpE1i0EFG6mVblU6RSEu21xw4sUJHOt6DE2KN5FfEYDLry+j1qZaqGhVEb79fFMcW6N1D1w94KGwG5M4NqTLRdyZEUiGgEzCQDciderUCb1795YaRP/patasCS8vL9jb22sEpMogDBOWbMDsJX2A3oBjAUfc6ntLKVgYmuVFTHgw1jXeiK8+n9HzYk/kLZNXKbq0QSgHBcV5MfxtOHb+uRPbYrbhcunLsLvXHJ06tEfPds1Q2DJ3iopoN6zQCx20RmsUqFIAfW/2VZxB6ZDEhCEdYGlxVyYM8js3R/6iwu7h+j3H0btd2sUWKX+B8hgof4HyGFJqb66+QVx0HGzq2kh9HBYVhsNPDqOebT0UyFEg5bGfvsC2aFHERYZj1NzVwnWrmWkcGzKDHo9lBACZhKFfv3549+4djhyRPj8oOap0/fp15M6d8kOD2IBVBmGwqVwXr+IvAC2h1IJq+cs64aP/TUy3W4Bfj76jm3c3FGtcTGwQi8YeDgqKc8Vjr8fwbOOJ58WfY5vDdnxcEwpLC7NUFbQfNB57V8+BW/5BWHhiMg7FHBKOLQysMhDjao5TnHFpSGLCoDKoRa2ICYN87knMXzAwRuS3cBgbpn59Kh1RpKOKJXOXFOoLyWpLiyxF2OswDH81HGbWqX93pCSj55jZ2Dx/Agxy5EbwhzfImT3juZMcG+RbC9yLEZCFgEzCcObMGfTv3x+1a9dGgwYNkDdvXly+fBkHDhxAlSpV8O+//2oMqoomDE/vvsbkSqPxvsR7XOp6SbhSbkyNMUrBQ7ItO7T0RFg81ofrP65w6OugFF3aIJSDQua8uOP+DjwLeYb2Zdrj47KPuDTrEi7XvIzb1gEI2f4iTeFXfP1R06Es9ExyIDYyHPse7UO73e3QqnQreHX0SnO8ojowYVAUkpothwmDfP4bNmM5lk8ehsIVa+H6P7uE2ivl3cqj1vhaKQrw8PNAjwM94FbeDVtab5GpZHXZ1fjs/xkDHw7M8M54zoLFEfH+BZr2GIajm5bKN6EUenFsyDB0PJAREBCQSRjow507d2LevHlC7QVJo/oLM2bMEAiEpjRFE4ZFIzzwbelLBBR8iS19N+Ngp4NoUSr5LRKKwKf/5IVYN+NvOFVvjxM7/xWuqOMmGwEOCplbHRbzLBD6MxTDqw3H/JrzUb59ZXwt8hk1i7bFnr9XyiU8Z6GSiHj3DEs99qF5gwqwW20Hq9xWeD3itVzjFdGJCYMiUNR8GUwY5POhpPZBtxFTMapCKxzocQBl2pVB+z3tEwUsvb4UNrlsBPJPCcuUuLyq6Sph91BWOzrwKIIfBaP52ubIXSpjJxJW7TiEwV1bQkffCL4P/DN8zSrHBvnWAvdiBGQhIJMwXLlyBeHh4ahXrx5ev34tkAZra2tYWCS/LUHs8CqaMAx2moK8N3Vxt9wT6E2MxqLGi1DErIhSYNhy6DTcWzaEmXVpfH31SCk6tEkoB4WMe/Nt+FvYr7YHnS8ublEcO52Po0rbEtCx08fDdf6ws0wo1JZWc+k+BCe2rkS7MgNQ6WUheDfzxqWylxA8Ohi5TTL20JCWzt8/Z8KQXsS0sz8TBvn8amBqjtjvX+F95Tb0DoXgyrwrqDutLupMriMIoO+GwksKCzf1fRj5Qfjd4+DHyJc9HyxMlP9MICkoZ1erOfwvHpZvUr/14tiQIdh4ECOQiIBMwjB06FB8+/YNlLOg6U3RhKFGlU6I8nmOyj1aY92mCUqF53NoOPLRuXE9fcTHxiDkeQh+hPxAwaoJFXe5SSPAQSHzK8J6iTV+BPzAH5EdcWjjKpSp0wIPz8t/jfL+01fQtmFNOBrXRPOfDfC19Fcs7bQUZ93PwtnGOfMGyiGBCYMcIGWBLkwY0nby0Ys30byOE/Sz50LMt1BhAMUZoxxGyG6ZXfj36YDTaLi1oVBXheqrqLrdvP8UThXLAr9isefERbRrlPJRqdTs4tigaq+xPm1DQCZhWLFihXATEtVh0PSmaMIgOXKxaf8J9GgtXaVSGVgZmVsi+msQvJachN+IqyjuUhxdj3dVhiqNl8lBIWMuDH4cjDyl8wiDey7qiXyT8yE+DlgXtRaTN67HyJ7/HU2QR4OxuRXiv4ZigtFExEf9wpxxc7CkzRIMcMzcTSfy6KY+TBjkRUq7+zFhSNu/nYZOgueKmShXrxXunUk5z2j5jeUY5j0Mg6oMwsqm8h1NJM1fnnxBxIcI4bvF1Mo0bWNS6VG3Q19c2LM+w9escmzIFPw8mBGQncMQHBwsJDsvW7YMdeokbEtqalM0Yfgfe2cBFeXSxvE/KI3SAipKGoSCWKCCioWFgi02InZ3X/WaF7s7sLtQQVQUEVQsUjAQAUEapOM7M/stgiC51DLPOfdchMnfvLvzPjNPCAgIUBSRMfFQkCk8nBwvWanod8b3d26YvHU5Gi6vC4EsASwIWwCJBpzTHya/CbBNoXRPQ/LPZDgtckKkdyQmv+SEQN2gsAGZUZnwUfXBtZ9OuaeOpWnZZMgkPL1yDEO6zEKHlSpY+HwhZneYjR19yu60WJr+mcJQGlr8W5YpDMWvrVLLdojwf4VFWw5g88IphVbg+iwQZYEoDSWVO1Pv4NWBV+i3vx/a2rUtabVCy0XExEOlqToykmKwcPN+bFlkV6r22N5QKlysMCNQgMBfbxjmzJmDO3fu/BXZq1evICVVMxxweakwnN54C+7LHPFO8hPcEu9XyiNlOtQGrpePQm2BAXQfNYZhpiFsj9lCuY0y9PbrIfJXJCIWRFTKWKp7J2xTKPkKxX2JwwH9A0hLSEOWQBZCZ4vi6PZlCHgYgDUz1uDi0IswCZ6IRydLHxHt6OV7sBlqDjH5xrj0/AD6n+2PPpp94DjaseQDLEdJpjCUAx4fVWUKw98XMzwqFh16WiDk7VNa6OPXUEj/EsJxk+NQ666G1vtbQ0ZMBgriCuh6oiueBD/Bw7EPae6EkorTQic83/YcPbf2hPEC45JW+2u56WvssW/tfJD8RJHfv0JKUrzEbbK9ocSoWEFGoFACf1UYnJycqLPz38Ta2hoiIiI1AisvFYZlPbZC5GEy/JTCcD78YKXMnxvyrqFVG4TpeaGHeg84jXGifStuU6QKQ+i80L8mwMk7SOLQei/oHtIy0zC29dhKGX9ldsI2hZLTHmAzHw2OJ0AwG7iJm4gSTYavnz+SU1PRZromkAq4H36Pjtp6JW80T0mherLITIrF4WuXceDmRmhpa+HcgnNlaqu0lZjCUFpiRZcPjYxBowYV79zK21EDTGEonOgr7yCYmvVAcmQwDYF8xOEyNa/1veyLS0MvIbpNNHYP3I2FxguxpeeW3H0mbH4YlCWVS7xM706+w9sTb6E/QR+tx7YuUO+CzwUsdlqMoTpDsbXn1hK1K920JeK/+aP/xLm4ddS+RHVIIbY3lBgVK8gIlE5h4CdevFQYZjRaAoUwMXwxTcWJxxsrBdPlB08xtLcJJNXVkTT2M0TriuLXsl94f/I9loUsg2O2Y4kdSs95n8OoK6NoG4lLE1FXsG6lzKGyOmGbQslI+3z+Bl2NphCHOLLlZICcbKTGhEOn60A0UFTCowuHUJ6IJGQU+j2H4J3zFdjqL0XDtyIwXmiMnlt6lmyA5SzFFIZyAsxT3XreWlw4tAM/Qr9BTqoe7xquhJaYwlA45Hb9RuHV3XMQkVHC3Xv30L0952We5F1xWeGCN2ZvcKPLDRr9z3uaN+ptrAdJYUm6Z/BSyOGVuYN5vkOw4to/ce0BJlj2pmFW3/n6Q08rfxbpv9Vne0NxZNnfGYGiCRSZhyEmJgYkgRvJ+Ez8GXR1dXHr1i3Iy8vDyMioxrDlpcKg1qILJAJiYb1xEZYsqZwT+tiEX5CVkoRAXWFo2DdBUEwQXExd4NrNFTnCObhgdQGz5s0qkUPpEucl2Oy2ma6d+yR3dGzcscasY0kGyjaF4imRuOjT/9mKi+e3QKV1Z3x7+xSvfYPQrrUOcjLT6XNG/l/WaCTcEWw9ehGLbIZDR74LhkaZURM629e2xQ+QByWYwlB+iJ9CfqBrfyt8f/+cNjZm/lqc2raq/A1XYgtMYSgcdl3x+shKSaRhVHsb508E+tX3K1oda4XEehzlgPgtzLg7A0aNjfB8EudZ4JWEJoaisX1jGp6Va1abEp2C5KjkIvM2NO9kjo/P75UqghvbG3i1aqyd2krgrwpDeHg4zM3Nc5O2bdu2DRYWFtiyZQuuXLkCkqehbt2acTrNS4WB+0X71v9TmRPIlOVhI7bgqdGh6H3YAvdDb2Bb+22QWS+DkOchCFEJgeQhyRI5lPZ16AvHII4d+eYem7Go06KyDKfa1mGbQvFLc7zLcXx79g0OcMCo3cuxZsY4Wslq6hJcPcBRJuU1W+Nn4NviGyumRB0RCQimp2O90gZomWnC8oxludssSQNMYSgJpb+XcXJ/g769zKhJGVdE5RohJep7uRoeZLsQ1w+VzPSkXB39vzJTGApSXLrtEDYtnAIZVW3EfPEpUMA12BWmJ0yp6ZFdWzuYa5kjICoA9UXqlzpBaWpsKoLuB4EECtEZrlPoknKTRcYsjoGMqAyuj7uOd6feQdNcE6PvFh4N8I3/J7RpqUnbO3v7EUb261rs48L2hmIRsQKMQJEEigyr6uzsjL1792LVqlVUWSD/eXt7Y/DgwfTmgSRyqwnCK4XBJygYulqqENSRwPzt0zBMZxjaNixf5IeS8lM17IZgr8cYsGUCbiUfp9k2L1lcwul/T2ND8AZo9tTEPet7xTZHku+QJDzXhl9DT42ekBDiRFra8HQD+mr1hYGSQbFtVOcCbFMoenUCbgbgvMV5JCEJByTPIDExHDcCbiApPQm9NHpBo1knZKQmY+2m/+jtQHmFexI4avZqOOxYU97mSlyfKQwlRlVoQRJi0/vRDciq68Hl7nWY9uyL+JAArN1zEquml+1mtbWZJd67XEMXq4k0iENlCFMYClLmRt2zWboJh/9dXKDAbs/dmOU4CxP0J+CYRfnyMIV7heOQ4SEotlaE3dvCoxpxHapJfoeOEh2xTXEbHdPsz7MhrSb918fEfNws3Du1Gyr6XfDtjWuxjxPbG4pFxAowAmVTGLp06QI7OzuMHj0aEydOzFUYyEbcrl07esvQqlWrGoGXVwrDppEH8PN8IDzavoNb/4dYYbIC67qtqxQGPUZNxcNzB1CncX1k2STQ7JrRi6LxKfYTNHdpUlvTr3O+FjmW2NRYkNMcoiQkLUvKLZuYnohG/zWCcB1heEz2gIaMRqXMqSI6YZtC0VQD7wRiw/hdeB31GEqjTeB0Zi8MDhrg7Y+3eGX7CobKhjxdlsVbZI9G5QAAIABJREFUD9Lwh/Ja+vj58Q1P2y6qMaYwlB01MUXSbMJxbH3m5YNOBtqYv3EP7JfNRIPmhjQEZ2ml7/jZcDy5K7caiXazZ/Vc+m+SOExBRhrt9ZqVttliyzOFIT8iru8Ssf8PCQ3L58ge+SESx02PQ6atDL4t+YbWSq0xVLt0+Vf+XJDE0ETYN7anORjmh88vdL1mOs7EHs892N57O+Z0nIOIdxH4/PAzjOYVbfZMTHWVmqrTHEUjZq/EuR3/FPk8sL2h2I8LK8AIlE1hGDp0KNq0aYOlS5fmUxg8PT2pEvH8+XMoKCjUCLy8Uhjmaq6C9Kc6uGf+CC86PMHlYZdh1dKqUhg8fvkBQ0ZaI/rTe2AcINiwLvzm+0BDVgMi60SQlZOF1BWpEKlTdOQqElHpZ/JP6Cj8vh7e4rYFi505J01NpJrAc7InFCUUK2VevO6EbQpFE/3yPQLqKkq0kMf7ABjoqEF0vSj9N3l+hASFeLokJHa6khznlPC1iy/qfs9AqzEVf9DAFIayLyOJnnX7qH2Bk1thJRlkTIqDUv1GWGgyD/OM5pWoE24oTEFhMVhNnotLe/+l9QbazIer013EBfvRfxPfGXH5RnC+74iOrZqXqO3iCjGFIT+hIdOW4sr+TQVs/9OT0rGj6Q6kxKRAo5cGrO9bF4e2xH8/P+g8ZDVl0Wtb4UlOD70+hCm3pxR6o/Er8leR+YYu3nPFiAG9kJOZhi1HLmDhpGF/HRfbG0q8ZKwgI1Aogb+aJB08eBD79+/Hxo0bcfbsWQwaNAiamppYsGABpKWlcenSpRqDlFcKwzzxJZBKEcOx6SfwTeErAmcGQlOWY0dZWTJ/0z7s/ncVMhKjMe/f3Zjf0xLrrdcjpF4I1t9ej9aKBUPXFTY2sjGIyYrRPzXY2oAqEQ3rNURYYhh0G+ji2cRnkBKpGXk28s6PbQpFP4ncrK6NW3dCyNtn8Ar3guEhQ7RSbIV3du8q5DFuYmCC0LduWCG0CoIZwNyQuajfuGITHjKFoexLKVxfDhmJMdh27GK+DN+dp42CmyInLO5MvUXYZcnxd5n5zw48fvwY4d++IPZ7EAaMnZbrp0Be4hZPHkHL/Xf8EuaNH4KRc1bh/M6/38yajbSD89n9ZZ9AnppMYfgN42dsAlRbtKKhVA9dvIPJQ/vmY0xyJgQ6BmL4leFFOhzzZGHyNPLi+wt039MdI16MwJ7LeyAuL47UuFQ49HVA9MdoLIoq2s+Oe4spKCKOuy5PCzhxc7tiewOvV461V9sI/FVhyMzMpMrBn8nbVFRUcOjQIao81BThlcKgqt0ZaZ8/4cfSHxAXEqehTatCuOYBUk1awPuCE44aHYV0c2nM9p9douGQkyT1HuoYeHQgDnsdhu0tW6hJq+Gl7Uu0PdQWEUkR+LHgB3Vyq2nCNoWCK0ZulUgUkhC3EEzrNR/vkz0wYfc/1Nn56JujsLlpg/H643Hc4niFLLfdqm04uG4hxtWfCrUERQw4PABtbPJHZuF1x0xhKBtR7neLmIIKkiPz5+HZ+eQg5jy2Az4DuAo00TJBeJAPPbz4UywmL8DIwf0x0qIPcjJSMW31f9i75veNhE5XC0RHhGH0uAn4b8k0Wt3+xGWqoAhJyiK9kDbLMiOmMHCofQj8is7deyHheyAkFFWR9OMLfkX8gs9FH7Sf2Z6WyUzNRF3Ryg9kQvyn5mjPgconFaj3VMeYB2PwPuI9nAyckBSeBJsXNmjUoVGRy999xBQaClpJuz3CfTwKLcv2hrJ8glgdRuA3gSLDqtIvmg8f4Ofnh8TERKiqqsLY2BhiYpyT6ZoivFAYPD98RIdWzVFXTxqZVnEwVjGG20S3KkPATYp19KIjvo/wRE52DpYnL0fQvSAkhCZAf5w+HZvTIieQ6+YWg1ugpWVL+rvtKtuR8D0Bsz7NwtqPa7HTYycO9D+AKYZT8C7iHUZfGQ23SW7shqHKVpd3HXc72Q2Pvz5G3JI4HB1zEYmXwuAp9AZ30q/TTki4xL0v92Jnn52Y1WEW7zrO01JgcBiaqTZCS7TEQpul6DS5A5q15729et7BM4WhbEvZSNcIYT4vMHruGhxatpCe9nKFBEZY4bICct80EX0sKPf3JJZ/z8Ej0bWzEQK/BFPlkAgxQcpOT0HvMTOoc+rfhDjGkqzj5NZz/txVcP/ljIXb92HdnIllm0SeWrVFYQiJiMbyrXtxzeEEfkWHQbieLCSk5VFPWhZyCor44O6CjIQokEhXz1wfQ01YFie6ngDxMZjkPgmNOzYuN+u/NRD2KgxkjVW7qkKumVyBYtxADPUa1YPtS1uECYdBZ58O7APsEeMQQw+2DCYWH4yDuyfefuKBfiYcJSivMIWhwpaYNVxLCBRQGFJSUmjo1GfPntGwqQMHDsSkSZMgLCxcY5HwQmGwm7EJB/cuhfJYPYSrf8C0dtOwt+/eKmPSZ+xM3D+9BxodeuDaLHsotVaCgo4Cjpscx7en32DpYInv7t/huceTjrHDrA7os7MP/fmq9VV8cPiAjnM7ord9bzz6+gjdVLtV2Vx42THbFPLT7Hm6J5w/O9NY6h96f4JyrBT8jeNxzo2TIdX4qDHcv7vDdYIrujTpwsulyNeWYnNDROp4Aa2BIwOPYJLBpArrizTMFIbS4yUmQwsmDoNeHQPM6jQDoa4h6L6+O7os5zwX5CaK3Ejt77cfwt/UsGX7LkwcN7ZANC2dLYbw9XgHxGVBN8cCH1w4yunfxHmxM9y2cA5fsoVycCTjMKDTBKHe7qWfxB81aoPCsGrnMaybU/znidwseDx7DB3Npngw/wHc7d2hZqaGwScHg7ysV5RcHX0VH85+wKCTgwrN9kz6fXv8LZQMlKCkrwSjo0YgZkoyqTJwnOSIDpodSjQ07p7YuocV3jpdLlCH7Q0lwsgKMQJ/JVBAYZg1axYcHR1hYmKC9PR0vHjxAjY2Nli8uGD4tZrClRcKw3i5GWgcIw1P6w9w0ryJg/0PwtawcpJQFcaZG+KV/O3lh0C01dWktwbk9iBLPAvNXZtjiOYQvD78GuJy4jRpFgltRyTSOxKf7n+C0XxOFIq0+DSISBXtLF1T1pptCvlX6qLPRQy/PBzNZVoiZ3YWmjdsjjlH16B7nzbIzsmmGVyTM5KpeR0xs6soGT1nDc6+WQt0BxYYL8A/hv/k+tBURJ9MYSg9VYkGTZD8MwRzVFdA+mtdiCuI0zj4Dds2pI2ZnTKDyxcXGr65t0bvQjv4GvcVajvVcv/Gvbn8szC5CdXswzFrJafPrutdIashC3mrpmhjxHF45kZoKv1MfteoDQqDmFxDmqWd+Ar9u241urbTh/+Xb/D/9A1e733g6+ePqMhwPLh0GmqNOXsAOVTyvewLvVF6xZr7lIc/qXt/3n282P6CZnkn2d6Lk10eu7DIaRHSstIgLy6PD1M/QDpTGqLSnOAMf5O8e6L/l+9orprfjIntDcWRZ39nBIomkE9hIJmdO3ToQPMujBkzhtYkjs/29vZ48+YNJCUlayRPXigMswRnQy5HFk4LH8JN4ik8bDzQvlHBa8/KBMSNcd++/2h43DqDxLBEzJ49G/5J/nDv6I5F0ntgv3w55FS0oKNviIHmvTB7bP7EWcSu/Wz/s/RGQquvFh0+uX0gm3iv/wqPalGZcyxtX2xTKEhMboscYlJiAGKS3gSwbmWN04NPwz/KHy33toSWrBY+zvxYWtSlKv/ifQCMRrUAhgL9PPuh3cN2GOcxDqqtVUvVTkkLM4WhpKQ45bjO8MS8KOjuS2rfrmKsQpUGrphMMYGHvAc+zPuAZnKFm5RdHncZie0S8V7rPXa82EF9oz7PJk4PHPny8AtuT72NmKAY2L2xyz3EyDtazY498cnDGV2HTaZ26eURflcYxi/agJNbV0BcQQW//vA5KQ83Xtb1Pu+NIMcgqpxo9M4fsvvNsTdQNVWFjIZMvi5JuPBhl4bRoAzGdY0x0H4gOszogK5rugICfx9di859EeDmCJKj4e6JnfkKsr2Bl6vK2qqNBPIpDL6+vjTfwtOnT6GkxAm9SDI+k9uGGzduQFtbu0YyKq/CcOzqffhZOUMSkugY1JG+aE1uM7lCT2RLAvrcnccY1b8bxCGOmdLz0VixAca+G0vNTHx++gAnyQ6dv6W80TGSo5Kxvcl2ZKZkQn+CPiyOWcBxpmOuGRPXdtR5iTOExITQfH5zKElynovqKmxTKLgyc+/PpS9vROpCCI8muKBzk85UiSBZv0XrilZKeOB6eqpIsghGX6e+aO/ZHn56fpjnOK9CFG+mMJT8ExrwNRTazZshOz0ZxLxl7awJBSrv1dmLKN8oPOj9AK6OrqgjUAcxgTG4Nu4azd4to8554TvQ+gCiA6Nh/dAaJh4m+Bb/jSqnREklcnHIRfhd8YNUUylqCtPUtGm+vlKiU7B71Tls2DcXSSKZcPP0KleIVX5WGEgeAsVGTZCRFFOuhHolf1J4W5Kbo0FIXAjLfi0r0HhUchQNwiHuIY7h5zlJJE1WmKDbur+bz5659RBjBvZAXUkZGukrr7C9gbfrx1qrfQTyKQyvX7/GiBEj4OXlhXr1ODaNaWlp0NXVxalTp2BkVHQileqKr7wKQ68xM2iCq779puHO7arzWyiM78LN+7FtyTQswRKIQhRy+/WwLfAfBNd/BzwAhhuuQKf2hjh77wJeOJyHeIMm+BURTJtyWe6Cp/8+pQ5lRDkgQmxJb9neQrtp7dBza09E+UfRlwAiH6d8hMMBh+q6zHRcbFMAgmKC8oX7NRhmgbqpYZBIkMTifxbB3MS8Staw/8S5uOOwA42662DCo8EIUwzDj40/cGfUHZ6PhykMhSO96uyG6Nj43JCaxOzHYsgIxHzxxjhJO/Ts0hnmu8xp3HyuXBp2Cb6XfCGiIQIRWxHMXTSXOihvkdtCi+iO1IXVWU4+mguWF+B/zR+yWrKQuCCBSTcn0RusgJkBEIAA4r/Fg5wq05PiQuTyiMvwueADFzkvuGbehJi5AvwOvKSJKcsi/KwwDLJdiBuHt0FWTRfRnz+UGM/9ufcR/iYcZhvMoNJJhWZ6J35OoQmh9DBsnP44LO5U8SbIH29/xLkB59CkSxNMcC2opJIJke+y9ofbQ/SrKGwv28Lmlk2xDtrSTVsi/ps/Zqzdjt2r5uRyYXtDiR8RVpARKJRAoQrD4MGDc52cs7Ozac4FcsugrMzJ/klk+fLlNSZaUnkVBlk1HcR+9cWuM9cxc7RFtXuUlm8/itB5T9EQDXEGZxBiEAJYAAbineG18Ckdr8BaAQik10HO3iz0HsiJWhLlFwWP3R7ot7dfvmve5J/JuaYIJPqG/YxDkL6cDm9db+jZDcCEIf2govg72gW5Nr718RbNATGoxaAq5VPbNwXXYFeYnjClJiPvrN9hwIz5cHbYh0kKk6DyUwXW96wLmAVU1oK5eL6DWQdO9K4zl+5gov9gpGelw3uad75EgrwYD1MYCqdIHF9JHH4x+cZQbKqF4HduyMlMh4ikPJalzUJORjb0PPVg2e636SK5SSD+UardfpuPPd/6nEZgIwcLZhvNIFKf4wNFbivPDTyHvnv6QkpLChq7NOgtw5VhV2DZktMmyfny8PNDdFfrTsP9cuVO4B082/sMojtFodhLBVPFbQH9LGimtEXgppdleiz4VWFwcn+DPt26IDvtF45ccsSkIZyAFiWRvdp76Xf/RLeJqG9YH43+a4RfGb9DhJtrmuPu6LslaapEZcgzQZSDtMS0fNGOgp8E49HqR2jUvhH1b/ibkKAM5NbcSNYIz2c+zy329fFXGnnpT5m7YTd2rJiFeo20kPD9t6llbd8bSrRYrBAjUASBfAqDt7c3iNNzSYSYKHFvIUpSvirLlEdhCA6LhGEjbWTUBeIzoqpyGkX2/c/eUzhx4iRUNTQh21wcVwR30Jcw8jL2Je4L1HeqQ6yOOFJWJQM5wIPnXuhpVHioOmJvvuPIGTx64IjIgNe0XzULNXwx+AIcBhAKGPQaCocD9mip1hj9N/ZHoz2N4NPFB4/PPkZdwcqP5c2FU5s2BRKK8sfnH3hX9x0cYx1xN/AufRkjovGsGYY9HIILOecRjGCsqLMSgjkCWBK3BML1qi7imW43C/g8vgm1tt3RZ01zGvL1qNlRGLXg7e0lUxgKfl30mzCngF03/Wy37Y5zS/7DvSE3ENkgEk9WP4HHZA9ICEkU+Z1DYuRLKhft13b4yWG8HP0S9QTrYeWTlZBWk8Z57/MYeWUkdZwmDtREUjJT6IurQJgApl2YBr3hehguyzFDwX/Alu1FZ/H920D5UWFwfeWNHt1MqSlS88594f/0DsjL95/mXYUx4d4MkXwLSxOXYvmT5dj0bBM1C5xoMBGyYrLQa6CHFvIteLbXEX+YbUrbICYnVmwStr91+vTb03xR3E71OEX9Yf6Wo4EbYnX/+VuwG96fNlub9gaeLR5riBHIQ6DYPAz8QKs8CgNxBpTdHQ1FKGKK1xQa+q26S0Z2BkTXcyJKpK5Ixel3p6lpgEVzC6TeVaHhWIk01DWCnoFh7nSysrPh8dgJiaGBub8TEBKBim4HZHeNxncpHygF6SL+fAiUM+XwRSgUxuOs4FPvLuZsn4OE+glo59YOI3Q5mV2rQmrTprBWZx3gm43DtocR2jCU4q6bJAbh1wKY8XgGxHPE8QRP0Hr9YJjHNMdn58+we2dXFcuS2+cb/09o05ITHeforVsYoNYBDv0coD1EG7228c7JvrYrDOSgY8HUzciQAq6f+g/kRLqXMSdZ3vYTl0E+66fPXkDLTuoIV/XEmq5rMH3LdKQnpmPHqh3op9WPJ88JyR78fNtzeoNJEnKRhJF2t+1w8PVBbDTbiCWdl+T2c+LtCUy4kd80Re6HGqIPcByxFm05gM0Lp5RqXPymMDx/549upiZIj/8JFf0u+PbGFV6HvXBryi0YzTNCj009IFhXsEhGEe8iaAZlub5yUN2hitTMVHhO9kS7hu1KxbbEhXOAtXXW0uKrs1YX6bRc0jYfLnuIZxufQbSBKKL3RWOLFcc8jivcEKuqht3w5ZUL/XVt2htKypGVYwRKQ4ApDMXQati8PWw/9gUEBbAqdSUEhYr+Mi4N/Ios2/FIR8SlxuH2qNtY+2Qtzrw/gx19dmB2h9kgp7x+L1yQnZpU6BAkldRg0KkbLAf0xZxxHNtkUn/MtTGY8n0KlI9wTNN2YAfihsQBusCMvTMh/1MO4XbhOLCf4/NQFVIbNoXQyBjYTliB9ncVkSqSit02h7F61GJkfJPF8pE2aCykjsnCE1CnoQha7DWCVc/OIA7uP978oJlUq1pMh9rA9fJRKOt0gPveCzjV8xSyM7Ix8dlEalPNC6ltCsPlB08RERVL0Z2/ch0vb13GtLo2yMrKwkXRe4gVjEdaXAS0TQfC5/GNXMTcxH3cX/RQ7wGnMU68WALaRnRANB6veYz6jetTnygizfc0x8foj3Cf5I6OjTvm64vE3yfRcUISQiAoIEgjLA2wnJOby6FNn+FQUFCgdfR0tDG4TzcYt/77aTg/KQyvfYPQqYsJ0mLC0UjPGN/fc3JXbFXYSj/fJCkaORAoabbmmY4zscdzT76bHp4t/B8Nkdw/YjJi6LG5B4iTM0kmSnxZ5FvIQ6NX/shJJRkDSVRKcg65x7nj1KBTODT0EMa1HpdbNW+IVW543tqwN5SEHSvDCJSVAFMYiiBHvqB76rTHaIxGS0MdTHtVtaezZV1k5f+U8SPpB95PfU+vm7lCEjU5P36KnJwc+ivVJk0wxdoSBi0KfoFzzZrGXRkHtQ9qkGgggQZLVTEubhiQnQP1HepQUdfB47c3yzpMntTj901h7IJ/cHa/PaST66Jrs65IlE/Eg/cPYDliEdxcnBDx+Q0G2szHmTWrkZmWmRu9hidwedQIOf1WV1en9td2q7ZhnEwnelpIEjtxY/OXt6vapDCMmbcWZ7avyYesd93eMMo0QoZQBk71OoUQ/xCIq0mhe1MTLFCZT/1YSHbf4PhgtPyvJQRTBZEqmQr/Gf75HObLuw5/1iffQ+T7SFJYErGLYws1X4xPi8e01dPQ6FEjTFwykWapHzJtKa7s31TocEhEHJUWBmjboSNmjB8Jk7a6ueX4RWEgL8DtjDojJeo7FFu0RcDLJ5CS5IS8JT4mzzY9Q/uZ7Wnis5JIQloCFLcp0tsFryleMFAqPpNySdotaRmSB4K88JPQvcSXoiySGpuKm2E3aZ4ZBXEFBIwLgEyD3+FZuSFWdboOhPejG+yGoSyQWR1GIA8BpjAU8ThY2i3GtYNboNmxFwLd79fIBycwJhDNdjeDjKgMYhbnDzNX2gk1sm+E2J+xcGzuCNPxptjwdANWuKxAH+XBcJrjjKyUROj3HII3Dy6Vtmmela9JCsPr8NcYc3UMEtMT4TvdF/WEC2ZbJSFRN3TfgM37L2DrulX0hYGIqFxDLF7zL74nBuPRspOwgAUykIETEpfxLSwIMvWLtj/nGfAyNjRu4Xqc2raS1iZOm2PNzSAkIVTG1gpWqy0KA/c7ihCQ02gFwTp1ICQkAqORuhA9mQLP9p4I1OKYGFpetUSr963ozyTrO8n+TmT19NUQ3CeIjJ4ZWP9gPc/WoLCGzn44i9FXRxd5qk1Cq25R2EJ9rYiMvDkSKuYqWH38ALzcAiCSIYyMtAy8836Z62OVt6+e1tPx4P9ml/ygMJCwt206GCM58hsUmhkg4OXTIj/fxMn4TJ8zSLFIwdR5U3PReB3xguMsR+gO14XFcQv4Rfnhsu9lrDThfA4rUzx2eeDe7HtoN70ddZAvj5AgD5/dPmPS6UkYcmIIdEdwFEZuiFXys5CkLHpYjYHDrg3V/ruxPCxYXUagIgkwhaEIusQ051fEV2w+fB6LbP7vgFeRq1EBbR96fQhTbk/BUO2huDj0Yrl6IKYCl3wv4azVWYzUHUnbehL8BKrSqnjx/CtGDepLY7mPbjkLq7fNyE0EV65OS1m5JikMec1BZrSfgd3mu/PNlrDteqIrxHwaIOVSJP1bHbF6GGIzG+d3rcst27ubLYwfc7KaxnSrg50uK0pJrWqKE+XynfMVOifnJ27o2o5z+0VeGImDZHmEnxSGHSev4NipM0hPS0PzltrQadkc/h+D8DHAnzqQk3DKCySWoJmxOj2xbWnZEsO8hoFEzHKwdIBbiBv2vdyHkUEj0TGgI3R76qLFoBZo2I6TwXmf7j789PmJLsu7oPv67uXBXmxd21u2OOx1uID/wp8VSRLKTw8+4eujr/TmacjFIbj99jY2+W1C2uM0mP1rhrZT29Jqh7Zdhce91/AMfgnvII45lYi0IhSaaCAyOBCZKUmQUdFCYzUtjLcemWtmWexgq0GBTyE/oN+hE5LCP0NeszX8Xz2FnFQ9Gtb21f5XdM3+lNeHXuP2lNv018kzkrF592aa1fnSUM5BDmHXeWnnSp3dT9+f+O7+nd6AKBsqI+BmAHwu+kBnmA6aD+Rk9i6r+P70hd1oO5g5mdGADrM/zc6N8jdpyUZcOHGI7uNE6jduBldnR7RuXvWmmWWdL6vHCFQVAaYw/IU8iVc+sWc/NBFpBs8frmh7ti1aKrTEcYvj9Dq9pgiJRkKikuzrtw9T2/4+bSrL+Hd57MJmt800RvesDgWjaZ287oQ9Q1ahfxYnxJ/Bkg4YuLHk4f7KMqY/69QkhSE3AzNAfUuIjwmRqav/w707d5BY/zuaicjDva07sBswNhqDRdojEPHkOzR6asB0lSnqinEiUg2UHY249Gi4JnGiztQUIbbYYd7u9MbE/boLPOc8gaiUKMY+HFuuKdR0hYEki9x/9CTePL6DrOSEIlmQl6Kx4sZ4tOoRLdd3b194G3vTCFQnB5HsjaChTZtINSm0HefFziAvmUMvDq1wHxet3Vo0tn5h/gtFTdLe3R7HdhyjYySS0zUHVhesqInl9XHX8e7UO/p7IW1JrPm+CRkJf49op9K6Mw7t2YE+nX8HfCjXw1ZBlb+F/4Re+05I+B4IWXU9+L58CkVZKZqngjg5p8WnURO+0Y6j842AhCoeOmIo9K/q49aAW9hivwVmTc1o0jySbVl7aOUnYCXPmNsWN3Tf0B1dlhVUcsqLcOqdqUiZmQKRNBH8mP0Dg3sNxnj98bnN7j5zHatWLEdcsC/qSkjjyJmLGDfo76FcyzseVp8R4EcCTGH4y6p2sZoIoatfYQpTNLNphlGNR9GQc9GLomvUc7DcZTlVGEhyLF6GyvsbBHINvHvYOpindoV/3SDonumCMMl32NZrW6Vkxq4pCgPJsNzXoS+MGhtRx3TybBExGjgGL26doT+bwxythFth57ydaK/aGXcHX8c2xW30byQ8JTlJI9FniBCzhZsP3bBw0rAa9XxGxMRDS88QiWGfoKhqgPnJI5EcmUxNJmJMY2is/rJITVUYSMjMXr17UcdWrkgbNoNuyw6ASA4CRV/ip3QQRNIl0aCuCozVO2PloFloKd8SQY5BCLwbSE2NSOK00ghxQiXRdQpzmPUI9cB29+3ordkbE/QLT7BVkr5K4r/wt3aehzzHjJkzoB6tjhDDEHjKeEJRQhGvbF8h5VkK3p95T536h18bjhQZASzcsAM6LZqhvqQ4MjMzkZaRiWfunnC6chqZSRzncEPzETi3/z9oNeXctFQnCY+KhXbbTogL9oOMqjZ8Xj6DsjzHPv/T/U/U5IjkxRh0fBDNmp1XljgvoQc7jdEYERkRkJKSoiaPxM6/qsTd3h0P5j+gzyYxh+O1kKzQM87NwM0fN2mIXmLe6W7jni+/C9kbug+3he8Tjp/d9DX22LN6Lq+HwtpjBPiWAFMYClla4mCmb2CA6UljIQMZKB5RxNTvUzG4xWBcHX6Vbx8GXk0sMDgMI7rZwOuLI0DeL5oCB/sfhK2hLa+6+Gs7NUWemPIJAAAgAElEQVRhsL5qDYcPDrBrtgC6OZwTtx279yDoBcekYnCPWWjtLAtBkTo4PPMQQiRD6KlszoUcGvu+oWFDyDX/nTyvwsFWYAckuIBRxw7ISIyBSUsr9AzWx4tBL3Cn2R0EzAigSehKKzVRYSBJEpvrGSDlZwhNrNZr8AisWzgdU1xHgSSvKkyaf2yOJMMkfJ/H8W2pCCEhUEkoVBKn/+jAo2XuIiYlBsffHkd8ajz+6fZPqdshySf/lPNDzmO4zt/NRfP6MMR+ikWmbB0MnTIfTy4doU0JikjActIsXNr7b6nHU1EVfsYmoIVhJ5p9W0qlOT68dEOKZzSaDWhGTULJAdDm+psLDRBA/KLaHuKYar21e4vNzzbjnPc5WLeyxunBpytqyMW2+9npM96eeEvNj0iQDf/r/jQErLSqdLF1S1OAJKAjfjJkr5YXl89Xlbs35PX7MR40Dm7XTpSmC1aWEai1BJjC8MfSf/keAV3D9tTBzLLRRPRW6oAXK1/Qjc6+tz3mdmQnEiX9tPQdPxuOr3ZBvps8Bl4fhBXOa6HWvGJP82qCwkAik0htlEJ6VgawJQdI+U1UUFgMG/YcwyAFfdy0uUnNjjw7emKW4ywavcZtolu+7LglXYvqXo6YAFpt7wq0z4REsgR+iXMyz3Zp0gWuE1xLPfyaqDAotWyHCP9XkFXQwgunh9BqzQkxa7vOFhnHMvBT4SdybHIwrds0elv3ZtcbJG5PREq3FGxyKTyCUKnB/b8Cybp8M+AmNT8kflDkxHpdt3VYYVJ1/jEdjnTA94TvWGW6CqP1RoO8HJs2NS0wxYxfGbg84jLNAmww3QBpaWmIeR2DU2anqI07MeNRXqKN0TbTaB4DIiIySpi34h/8O29yWZHxpF5swi9otTFG9Kf3qNdQA09P3YbHYleEvw5H1tosrMvh+C7dt76PXhoF85YQX5Xpd6fTdSLrlZyRTANTEGbSorx9OS/rhA+0PoCI9xEVZp5E/HHuzryLrmu6oqlJ09xh5t0bNh0+j+UzJlGfO/K583zkCBVF/jiAKeu6sHqMQHEEaqXCQE7AF27Yjo/+/rAePQLLpnBsQEl8e21DI5pOnjhHffZ+RR3MuHa35PrbULl6270Wt+CV/ffdDjfwbtEtqISpIFYkHp2PDsCQ0d0qbBjVVWEg9sh2S9cjOzsbEXHheBt9BRAF6rjXg6yKJqYE9kdK3TQoazfG/LczKR/i2CgmK4asnCzo7tOFf5Q/lCSVcGPEDZqZld+ku70lHiVeo9NqmdkR0VKfIeEtgWWzlsGmjU2pplvTFAbDPsPhdf8iGolowjZzDI1kY+lgSef8weEDSBx7Is36N8PIWyNBkm8d0OfkOxl2ZRh1dOalDDo/CDcCbmBAswE0J8LbH2/zBTvgZV8lbevZt2fo3KR4Z13PPZ5wnOlIm1XprAKr21aoX68+DeMZ4hZCc32QnB9EiG370oXz8OsHJzkciTS1e+dOjOzXtaTD4mm5Fi3MUC8gAQENY/HOww3BR/zwZO0TZIln4Vrfa/DW9aZ+dHnt8/MOYNqdadR3hZggVUchzs/7dPbRiGjzQuZBVIaTYJSX4rTICc+3PkdLq5YYdvm3iSZ3b4gPjqcKS6BUHKwGWSAt9gdE5Rrh5q1b6GlUueFleTlv1hYjUNEEao3CICfWCtmSgkjNSsMHz1u5XEmEkXRhAQjUFUZWcjxnk5FsiVtut9C6lQbCk8LR8L+G1NE5fkk8TSbEpHQEFpxehl+LYiETK4Nj2sfRyEIbDxdfKfLEKzUuFaLSpd9MqlJhIKeyxE74z5f5D4FfYWzSHUn/fykh9IQghGad+uDKmf1oJCKF/xr+R6ESBWFR9KICgEMTQzHg7AC8+fEG6jLq+DTrU+kWoQaU/hr3FR23d0PEnq9ADDDDfDHkHcXg2tcVZy+epTbrJEMteSGQVJJEk85NoGypXKhtdk1SGEbPWYOzO9dCBCJYrrAa2T/T0dioMSY9n0RXLelHEkKeh9BcFf3290PDtpxbuvODzkPVVBUd5+ZPfsaLpd70bBOWPlyarylyu2WsYsyL5iu0DeIgfcL+BCweWcBsjhkMZhpQO36ukBdGYvdPbk36Z/aHjqkOSJhfh31bcx3MW3bpj4dXT+X6DVTogAFkpWXBSt0GumGNkCj4C3ZB89FSrTGIic2s/rNwzuAchKWFqb9TcUoTCZdKfFqqm/hf80fC9wT6bH9/8R3tZ1TMoUdiaCLsG9vTBHELIxfmhmv+/O4zHk97TD9LROYEz0GUQAo6mpkjKvAthOrJYdOuA5g3fkh1Q8fGwwhUCwK1RmGwDrKmwBOQAHvY02tIE1ljqLqL4Uj2IcSQNxQATZTaYHKyFZRaNsBY57Gwe2hHzZHMNc1xd/TdarFoNXEQWx5uwa6LuxDaMJQOv82HTtje+l+0tWubGwKPe6L+7dk3OPR1gNVZK3qi+jdJ/pmcW5dbpioVBpJd2zvSm9qTc6//H7/8gD7EiTX2B7VLtxg9AY2VldH0hQgyXsfTqC8kgRYREj899kssFLQLd04kpkxjr43FnI5zasSLW1mf086WE6hdsbFId/RKM6HNWD+xhoaJRq7DJ/ldmmgaUs+lwn6QfYGuaorCsPXoxdyQzZMXb4K1hBHCXoVR510BwYI2+2VlWtp6j74+os8aiUKkp6hH/2+lbQWxuuULd1vacZSlPEkyqbFTA6JZovCd6gsZcZl8CgNpk2Q43npwK8adGQfNXpoYe2ssSELBEXbzc4MOkLDa9+/dLTKTdFnG92cd4rvStZsVhvsZU6WxycTmmHB0BOJS49DnTB8Qp3PCnygLf4t0xYtxVHQb64TX0YzuK9NXQlCoYg/eSNhWYo6U99CJ7A0HNQ7SZHU+XXxw/PJxyEpxggNwQzyTn81G2sH57P6KxlGm9u89e13to3uVaWKsUo0gUGsUht7p5pCMk4BwsjCUDzeHUkcx+Fn5IcM3AyINxdH+pBm692iDHU13IP5bPBRbKyJuSxyWunNO2R6MeYCe6iwMW3me6ruBdzH4nCXS09MwaOsg6Kfro4FuA3qSeursFXyb6YeXWgFQTG0Itc8ce9uhl4ZCe0jBMIDEgfGk2UmQ3BDcePKkfFUqDP3P9ge5ZdjacysWGC/A5QdPMdJyADJ/xaNBIz08c7tHI7J8ffwVJ7udpCdf032nQ6pJ/ign5WHML3W1jHpRB/AO6ECjRfV/PhyGRi3wK/IXvrh8wYtnL7C47mIoaCrQ7MR/CldhSMlJqTa223+O0dH1Ffr3NKV21B0HWMP9ZtU5pfLLc8OdB9ekarHRYiztsLSAwkBu7IasHIIee3ugbmZdtF3YFv229KPVLzg+wZjhVshIjEYd8fo4ePICJg3hfWSfl3tf4mGoD1btWUD7MqjTFjO2rcTEOQNpGFySkIzcurVSbIVH4x7lRlKrqWu1U20n4r7GYfaX2Tx3di4JE7I3pMWkYaT7SJCoW0s6L6H5QLgyftEGnNzK8dGRUFSFuJQMBAQEMWTEKOxdM68kXfC8DDGTnrlqM5weXUTSx68QFJWkCVKZMAJVQaDWKAxB1kEF+IqniGPZqWWoL1GfvrSKK4jjxY4XeLH9BSZ7TkYduToYeG4gVnddjU4qnapiffiuT2KHv+3GMTyadge90k0gC1mcwRl0lekK9Vh1PNZ6jMeBj9Ed3WEgbICj40+iY/YwbFu6GDrqv+PIk+ReBwwOICEkAUFTgnD6AOdlqyoVhgefHmDcf+Mg3kQc85tux8zxIyGQnoYB8qPRETqYEzQbIlIiiP0ci10au2joUP3x+ny3xrya0PyNe7Dr31VQT1LAR3yEdFNtdDcfgMXTJkJfWxX1NtYDiTn/efJnyGXLoX7j+rldnxtyDp8jPuNC8wu4sOlCtTPRIH5UegaG9OapayMrWK41xeU6l7G++3rq6M2kfASefnsKk+MmNMO9/2R/NJBtUKDBiF8R6Le+H1qfbY2A6QG4M/sO7M/YwzTUFGF+kVj5eg++BrpBQEgEh89ez1UaVj1ahTqCdTDFcAr1KSqteD72hPvkF4gLikUIQnAURyHcQh771p3O7YM818TJm5yGE1Mwbtjl0vZVncqTRHwfb32keRhIpLfKEOLrc3PyTQw4NAACygJQVFSkvmA6+3QgXEcYPtY+UG/6O4kbySU0eewIGrEtrxAFYs/+gxg/uKCjOS/nQYKunLvjDJcnzxDg8x6hAV7I6ZUKkG3iNSD2oRGSv1VcRDRezoW1xX8Eao3CcOnpJez23I0rvlegJqMGkToi9Kq3SVITuNi5QENDI3d1SUIc8mLHpOII3H/uBbuxM5EpGIMY/VBMeDwWoqmicBv6BcIJMnj/8jlS239Bqk4qcA9o9aIVVS6iEY1PCnHQaGGIHomtIfVWACliKej0rBMGtBkA94/ueBb1DAuNFxY5eJcvLjSCSP9m/cs9Se7zEvkhEjvb78SPBj9wTOIY8AWwFZ+DhjGc2xKrc1bQHaFLEy55HfWC0TyjcvfN7w0QZ3HrmUvgfu8qMn/F5U5XVk0X0hOBn/HBWHJ/CTI+ZECtuxosz1jSl5Gn9k/hMt+Fln9n9g77b+ynPhBVLYExgeh2ohuiXqcg7WoMOgr1QJ9EjiPvf/P/wwW7C9T8kUn5CbQ+0BrvI95jZ4+dmNWpYKJJ0gNRGkg2dfISKQABLNq8CGIpYjQnhY2HDTpPHYPPng9BopetPLkZZ3/uBllDItq+2rB4aYFeLr2oj1vbhpxwpkXJykcrsefmHszaOQtxYnF4oPEAXweEIlU4Ef20+lGzI66QBHdSolJVmj+huPlU578T5eTcwHN0iCSZYROrJlRhIDL++nh82fMF3Z52w/h742lOC64EuwbD+3kQsjtK4rTDDSSc8sbT9IdIRjItIqOqgxatDWHauRP6de+Ezm10yozh6OV7cHR5Aq+XnggL8kFaXMTvtoQBDAc6RnVE25dtccj2EMTqiSFuye/vwTJ3zCoyAmUgUGsUhsBAzpd8Xul3th+ImUxX1a70ypdJ5RMg2aNn35tNO26Q2gBh/4ahjkAd+u8jbmcw2XkMhJLFMXbHOKikKyIHOTiP8whAAC2jo62D5PbJCG0YgYujLuKKzwWc9juN6e2mY0/fPYVOaIvbFix2Xow2ym3w2vZ1uSbtssIFvpd9YeNugztXn+PNFBcIZQnhle4rOFreh0PL00jamYT+B/tDsVXVv7CWa7JVXHndvtM4fvwEvrziKAJoDygaKmLUqdGQ+lUfOcIC6PNyODq2ao6wwDC83fsWL/a/wJGJR6BsoIxnE58hJSOlSk5ro/yiQF5EAqUCMf79eKSJpAHJAlh0YhHEI8VoRl5DW0Oar4QJbwhc9r1MTRqJrO26loYWLUyI0mB81BifYz/D4oEF+in3Q7t+7aA/gXP7xzWPE+glhBzjDCiKK2P887EQuy6GeKl4bJ+7nZaTzpDGrthdGLF1BHW4zSskMV6382Z4HvqM/lrjkwY+afwOXNCoXiMsN1mOqW2n8mbyrBUaZc5jlwf1Y+g4p2O+22cSnnf8gPHo4toFgqKCmOo1lZpJXRx6EYF3OO8K07yn4dGWR/A75Ycw/Wg8qOONED8vZHWOB94D+MGBXEesHpS19NDasD16mHbGUPNuiIlPwMev35GenpkbcYsk49vvcA33nV0Q8P414r8VNKUk7Uk1aQGNNnqIUX0L83M9oBihiHrG9fBs1jO4hbghYkEepYKtMyNQiQRqtcJAnMp6ne6FuUZzMVJ3ZCViZ11xCZBNWmMX53ZnqPZQ+oWdV5ruaErteadGToWfrx89Je7Q3gJOn1/Ay+M5IkPeI2dqOvD/G26V7ypIqJ8A96XuBcxQSMQR4sh51e8q2r9sj8nKkzFy/UhIKEqUeEFIttrIX5HUrvjmpJt4c+wNrftE9yNcP91Ckwx5DBMahlNjTyFcORyzO8zGjj47Stw+K1g8AXJtv3rHQdx7cg0/+78F0oEGGxtALkcOfvCDUD1ZqLRsAyPjTujbxQjrw+eCRI4hpj6RbpEY03gMlq9ZXnxHPCzxeM1jGh6TyHeh7zgy+AigDdRLqgdtH22MXD2SPitMeEug35l+SEhLwIGBB/Jl/f2zF6I0WF6wxIH+B6iDcV4h63Zo/3lcjDiCzJ6ZqPOkDiZl2UIxSx5PZJ/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stroke-opacity: 1; fill: rgb(255, 255, 255); fill-opacity: 1; stroke-width: 0px;\" width=\"139\" height=\"67\" x=\"0\" y=\"0\"/><g class=\"scrollbox\" transform=\"\" clip-path=\"url(#legend3d17ab)\"><g class=\"groups\"><g class=\"traces\" transform=\"translate(0,14.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">GFT</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(0, 0, 0); stroke-opacity: 1; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"133.046875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,33.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">CDC reports</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(0, 128, 0); stroke-opacity: 1; stroke-dasharray: 9px, 9px; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"133.046875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g><g class=\"traces\" transform=\"translate(0,52.5)\" style=\"opacity: 1;\"><text class=\"legendtext\" text-anchor=\"start\" x=\"40\" y=\"4.680000000000001\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 12px; fill: rgb(36, 36, 36); fill-opacity: 1; white-space: pre;\">Model 3-wk-old</text><g class=\"layers\" style=\"opacity: 1;\"><g class=\"legendfill\"/><g class=\"legendlines\"><path class=\"js-line\" d=\"M5,0h30\" style=\"fill: none; stroke: rgb(128, 0, 128); stroke-opacity: 1; stroke-dasharray: 3px, 3px; stroke-width: 2px;\"/></g><g class=\"legendsymbols\"><g class=\"legendpoints\"/></g></g><rect class=\"legendtoggle\" x=\"0\" y=\"-9.5\" width=\"133.046875\" height=\"19\" style=\"fill: rgb(0, 0, 0); fill-opacity: 0;\"/></g></g></g><rect class=\"scrollbar\" rx=\"20\" ry=\"3\" width=\"0\" height=\"0\" style=\"fill: rgb(128, 139, 164); fill-opacity: 1;\" x=\"0\" y=\"0\"/></g><g class=\"g-gtitle\"/><g class=\"g-xtitle\"><text class=\"xtitle\" x=\"226\" y=\"239.20625\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Week</text></g><g class=\"g-ytitle\" transform=\"translate(3.9716796875,0)\"><text class=\"ytitle\" transform=\"rotate(-90,10.028125000000003,101)\" x=\"10.028125000000003\" y=\"101\" text-anchor=\"middle\" style=\"font-family: 'Open Sans', verdana, arial, sans-serif; font-size: 14px; fill: rgb(36, 36, 36); opacity: 1; font-weight: normal; white-space: pre;\">Percent influenza</text></g></g></svg>"
      ]
     },
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    }
   ],
   "source": [
    "gft_df = pd.read_csv('data/gft.csv', \n",
    "                     usecols=['gflu','dlscflu09','degflu09'])\n",
    "\n",
    "gft_df = gft_df.iloc[302:519]\n",
    "\n",
    "gft_df['week'] = np.arange(303,520)\n",
    "\n",
    "fig = px.line(gft_df, x='week', y='gflu', width=550, height=250)\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=gft_df['week'], y=gft_df['gflu'],\n",
    "        mode=\"lines\", name=\"GFT\",\n",
    "        line=go.scatter.Line(color='black', width=2)))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=gft_df['week'], y=gft_df['dlscflu09'],\n",
    "        mode=\"lines\", name=\"CDC reports\",\n",
    "        line=go.scatter.Line(color='green', width=2, dash='dash')))\n",
    "\n",
    "fig.add_trace(go.Scatter(\n",
    "        x=gft_df['week'], y=gft_df['degflu09'],\n",
    "        mode=\"lines\", name=\"Model 3-wk-old\",\n",
    "        line=go.scatter.Line(color='purple', width=2, dash='dot')))\n",
    "                 \n",
    "fig.update_yaxes(title_text='Percent influenza')\n",
    "fig.update_xaxes(title_text='Week')\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "From weeks 412 to 519 in this plot, GFT (solid line) overestimated the actual CDC reports (dashed line) 100 times. Also plotted here are predictions from a model based on three-week-old CDC data and seasonal trends (dotted line), which follows the actuals more closely than GFT.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "Data scientists found that a simple model built from past CDC reports that used three-week-old CDC data and seasonal trends did a better job of predicting flu prevalence than GFT. GFT overlooked considerable information that can be extracted by basic statistical methods. This does not mean that big data captured from online activity is useless. In fact, [researchers have shown](https://www.wired.com/2015/10/can-learn-epic-failure-google-flu-trends/#:~:text=The%20paper%20demonstrated%20that%20search,into%20potentially%20life%2Dsaving%20insights.) that the combination of GFT data with CDC data can substantially improve both GFT predictions and the CDC-based model. It is often the case that combining different approaches leads to improvements over individual methods.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    "The GFT example shows us that even when we have tremendous amounts of information, the connections between the data and the question being asked are paramount. Understanding this framework can help us avoid answering the wrong question, applying inappropriate methods to the data, and overstating our findings.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "user_expressions": []
   },
   "source": [
    ":::{note}\n",
    "\n",
    "In the age of big data, we are tempted to collect more and more data to answer a question precisely. After all, a census gives us perfect information, so shouldn't big data be nearly perfect? Unfortunately, this is often not the case, especially with administrative data and digital traces. The inaccessibility of a small fraction of the people you want to study (see the 2016 election upset in {numref}`Chapter %s <ch:theory_datadesign>`) or the measurement process itself (as in this GFT example) can lead to poor predictions. It is important to consider the scope of the data as it relates to the question under investigation.\n",
    "\n",
    ":::\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [],
    "user_expressions": []
   },
   "source": [
    "A key factor to keep in mind is the scope of the data. Scope includes considering the population we want to study, how to access information about that population, and what we are actually measuring. Thinking through these points can help us see potential gaps in our approach. We investigate this in the next section.\n"
   ]
  }
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